☆ 4.6 Review

Oncologic Imaging and Radiomics: A Walkthrough Review of Methodological Challenges

CANCERS (2022)

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Article Radiology, Nuclear Medicine & Medical Imaging

Test-Retest Data for the Assessment of Breast MRI Radiomic Feature Repeatability

R. W. Y. Granzier et al.

Summary: In this study, repeatable radiomic features within breast tissue on prospectively collected MRI exams were identified through multiple test-retest measurements. The results showed that the effects of different preprocessing procedures on repeatability of features varied depending on the sequence.

JOURNAL OF MAGNETIC RESONANCE IMAGING (2022)

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Article Medicine, General & Internal

MRI radiomics-based machine learning classification of atypical cartilaginous tumour and grade II chondrosarcoma of long bones

Salvatore Gitto et al.

Summary: This study demonstrated the high accuracy of machine learning based on MRI radiomic features in classifying ACT and CS2 of long bones. The machine-learning classifier showed similar diagnostic performance to an experienced musculoskeletal oncology radiologist.

EBIOMEDICINE (2022)

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Article Radiology, Nuclear Medicine & Medical Imaging

Robustness of dual-energy CT-derived radiomic features across three different scanner types

Simon Lennartz et al.

Summary: The study found low robustness of radiomic features extracted from different DECT scanners in patients, with few robust features and limited reflection in phantom experiments. Future efforts should focus on improving the cross-platform generalizability of DECT-derived radiomics.

EUROPEAN RADIOLOGY (2022)

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Editorial Material Radiology, Nuclear Medicine & Medical Imaging

Radiomics in endometrial cancer and beyond - a perspective from the editors of the EJR

Daniel Pinto dos Santos

EUROPEAN JOURNAL OF RADIOLOGY (2022)

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Article Biochemistry & Molecular Biology

Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI

Baptiste Vasey et al.

Summary: The article introduces the DECIDE-AI checklist, which includes key items that should be reported in early-stage clinical studies of AI-based decision support systems. It emphasizes the importance of responsible and transparent deployment of AI systems in healthcare. The checklist was developed through a consensus-based process involving multiple stakeholders.

NATURE MEDICINE (2022)

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Review Medicine, General & Internal

Radiomics in Cross-Sectional Adrenal Imaging: A Systematic Review and Quality Assessment Study

Arnaldo Stanzione et al.

Summary: This study systematically reviewed the application of radiomics in cross-sectional adrenal imaging and assessed its methodological quality. The results showed that the methodological quality of radiomics studies in adrenal cross-sectional imaging is heterogeneous and lower than desirable.

DIAGNOSTICS (2022)

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Article Radiology, Nuclear Medicine & Medical Imaging

Ultrasound-Based Radiomic Nomogram for Predicting Lateral Cervical Lymph Node Metastasis in Papillary Thyroid Carcinoma

Yuyang Tong et al.

Summary: Accurate preoperative identification of lateral cervical lymph node metastasis is crucial for clinical management of papillary thyroid carcinoma patients. This study developed an ultrasound-based radiomic nomogram, showing good performance in predicting lateral LNM and potentially improving survival outcomes. The radiomic signature and clinical characteristics combined in the nomogram demonstrated good discrimination and calibration, making it worthy of clinical application.

ACADEMIC RADIOLOGY (2021)

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Editorial Material Radiology, Nuclear Medicine & Medical Imaging

A decade of radiomics research: are images really data or just patterns in the noise?

Daniel Pinto dos Santos et al.

Summary: Although radiomics shows promise in analyzing medical image data, there are pitfalls to avoid for reproducibility. There is a gap in translating radiomics research into clinical practice, and more high-evidence studies focusing on prospective research with clinical impact are needed going forward.

EUROPEAN RADIOLOGY (2021)

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Review Radiology, Nuclear Medicine & Medical Imaging

How to read and review papers on machine learning and artificial intelligence in radiology: a survival guide to key methodological concepts

Burak Kocak et al.

Summary: In recent years, there has been a significant increase in research papers on machine learning and artificial intelligence in radiology. Understanding key methodological concepts about study design, data handling, modelling, and reporting is essential for evaluating the validity, reliability, effectiveness, and clinical applicability of these studies. Mastery of these key methodological concepts can enhance the academic reading and peer-review experience within the radiology community.

EUROPEAN RADIOLOGY (2021)

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Review Radiology, Nuclear Medicine & Medical Imaging

CT and MRI radiomics of bone and soft-tissue sarcomas: a systematic review of reproducibility and validation strategies

Salvatore Gitto et al.

Summary: This study systematically reviewed radiomic feature reproducibility and predictive model validation strategies in studies dealing with CT and MRI radiomics of bone and soft-tissue sarcomas. The findings showed variations in approaches among studies, with some including feature reproducibility analysis, utilizing machine learning validation techniques for model development, and conducting clinical validation.

INSIGHTS INTO IMAGING (2021)

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Article Multidisciplinary Sciences

Repeatability and reproducibility study of radiomic features on a phantom and human cohort

A. K. Jha et al.

Summary: The repeatability and reproducibility of radiomic features were investigated across different scanners, slice thicknesses, tube currents, and use of IV contrast. Results showed that half of the features had good repeatability, with changes in slice thickness affecting reproducibility. A total of 108 features demonstrated both good repeatability and reproducibility, with most being wavelet and Laplacian of Gaussian features.

SCIENTIFIC REPORTS (2021)

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Article Radiology, Nuclear Medicine & Medical Imaging

To buy or not to buy-evaluating commercial AI solutions in radiology (the ECLAIR guidelines)

Patrick Omoumi et al.

Summary: In recent years, artificial intelligence has made significant progress in medical imaging, leading to the availability of numerous commercial AI solutions that require careful assessment before purchase. The ECLAIR guidelines proposed by authors from academia and industry offer a practical framework to help stakeholders evaluate commercial AI solutions in radiology, addressing factors such as relevance, performance, validation, usability, integration, regulatory aspects, and financial considerations.

EUROPEAN RADIOLOGY (2021)

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Article Radiology, Nuclear Medicine & Medical Imaging

Radiomics in Oncology: A Practical Guide

Joshua D. Shur et al.

Summary: Radiomics is the extraction of mineable data from medical imaging to improve oncology diagnosis and prognostication, with a multidisciplinary workflow involving planning, data extraction, and validation. Applications in oncology typically involve classification tasks and prediction of clinical events, requiring collaboration between radiologists and data scientists.

RADIOGRAPHICS (2021)

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Article Radiology, Nuclear Medicine & Medical Imaging

Quality control and whole-gland, zonal and lesion annotations for the PROSTATEx challenge public dataset

Renato Cuocolo et al.

Summary: The study conducted a quality assessment of the PROSTATEx training dataset and provided publicly available lesion, whole-gland, and zonal anatomy segmentation masks, aiming to increase its potential utility as a common benchmark in prostate MRI radiomics.

EUROPEAN JOURNAL OF RADIOLOGY (2021)

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Article Radiology, Nuclear Medicine & Medical Imaging

MRI index lesion radiomics and machine learning for detection of extraprostatic extension of disease: a multicenter study

Renato Cuocolo et al.

Summary: The study aimed to establish a machine learning model using radiomics features extracted from prostate MRI index lesions to detect extraprostatic extension of prostate cancer. The model demonstrated high accuracy in a multicenter setting and could assist radiologists in EPE detection.

EUROPEAN RADIOLOGY (2021)

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Article Radiology, Nuclear Medicine & Medical Imaging

Artificial intelligence in radiology: 100 commercially available products and their scientific evidence

Kicky G. van Leeuwen et al.

Summary: Among the 100 CE-marked AI products in the field of radiology, 64 lack peer-reviewed evidence, with only 18 proving to have potential clinical impact.

EUROPEAN RADIOLOGY (2021)

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Review Oncology

Radiomics Models for Predicting Microvascular Invasion in Hepatocellular Carcinoma: A Systematic Review and Radiomics Quality Score Assessment

Qiang Wang et al.

Summary: Radiomics models show promise in predicting microvascular invasion (MVI) in hepatocellular carcinoma (HCC) patients, although the methodological quality of current studies is suboptimal. Future prospective studies with external validation are needed to provide a reliable and robust prediction tool for clinical implementation.

CANCERS (2021)

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Article Radiology, Nuclear Medicine & Medical Imaging

Measuring the bias of incorrect application of feature selection when using cross-validation in radiomics

Aydin Demircioglu

Summary: In radiomic studies, performing feature selection before cross-validation can lead to bias, and it is important to conduct feature selection within cross-validation to reduce bias.

INSIGHTS INTO IMAGING (2021)

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Review Oncology

Understanding Sources of Variation to Improve the Reproducibility of Radiomics

Binsheng Zhao

Summary: Radiomics is the method of choice for investigating the association between cancer imaging phenotype, cancer genotype, and clinical outcome prediction in the era of precision medicine, but its clinical use faces challenges due to reasons such as reproducibility and generalizability issues. This article discusses sources of variation in radiomics signatures, reviews studies on feature reproducibility and model performance, and explores strategies to reduce feature variability and improve the quality of radiomics studies.

FRONTIERS IN ONCOLOGY (2021)

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Article Radiology, Nuclear Medicine & Medical Imaging

Effects of Interobserver Variability on 2D and 3D CT- and MRI-Based Texture Feature Reproducibility of Cartilaginous Bone Tumors

Salvatore Gitto et al.

Summary: This study investigated the influence of manual segmentation variability on texture analysis reproducibility of 2D and 3D unenhanced CT and MRI images. The results showed that there was not a significant difference in feature stability between 2D and 3D contour-focused segmentation. Margin erosion did not improve feature stability in 2D segmentation, but performed better in 3D segmentation.

JOURNAL OF DIGITAL IMAGING (2021)

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Article Radiology, Nuclear Medicine & Medical Imaging

Deep Learning: An Update for Radiologists

Phillip M. Cheng et al.

Summary: Deep learning, particularly through convolutional neural networks, has been successful in computer vision and has wide applications in radiology for tasks such as image classification and object detection. Understanding key concepts and recent trends in CNN design will facilitate the adoption of deep learning techniques in medical imaging and help radiologists keep pace with advancements in the field.

RADIOGRAPHICS (2021)

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Article Medicine, General & Internal

Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence

Gary S. Collins et al.

Summary: This paper outlines the development process of TRIPOD-AI and PROBAST-AI, including systematic reviews, Delphi process, virtual consensus meetings, and tool development, in five stages. The aim is to provide reporting guidelines and a standardized tool for the critical appraisal of machine learning based prediction model studies.

BMJ OPEN (2021)

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Article Oncology

Impact of Preprocessing and Harmonization Methods on the Removal of Scanner Effects in Brain MRI Radiomic Features

Yingping Li et al.

Summary: This study investigates how image preprocessing methods and harmonization methods can help remove scanner effects and improve the reproducibility of radiomic features in brain MRI studies. The ComBat method is found to be essential in removing scanner effects, while intensity normalization methods improve the robustness of the harmonized features.

CANCERS (2021)

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Article Oncology

Virtual Monoenergetic Images of Dual-Energy CT-Impact on Repeatability, Reproducibility, and Classification in Radiomics

Andre Euler et al.

Summary: Virtual monoenergetic images from dual-energy CT are increasingly utilized in clinical practice, leading to more radiomic analyses being performed on these images. This study evaluated the repeatability and reproducibility of radiomic features from virtual monoenergetic images and their impact on lesion classification. The results showed high repeatability of features within the same scan conditions, but reproducibility varied across different VMI energies and DECT approaches. Optimal VMI energy selection improved lesion classification in vivo.

CANCERS (2021)

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Article Oncology

Radiomics in Oncology: A 10-Year Bibliometric Analysis

Haoran Ding et al.

Summary: A bibliometric analysis of radiomics publications in oncology revealed rapid growth, with major focus areas including artificial intelligence, segmentation methods, and applications in oncology. Frontier areas of research include reproducibility, statistical methods, genomics-radiomics relationship, and applications to sarcoma and intensity-modulated radiotherapy. This study provides insights for researchers from various disciplines to engage in radiomics-related research.

FRONTIERS IN ONCOLOGY (2021)

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Article Medicine, General & Internal

Development and Validation of a Radiomics Nomogram for Differentiating Mycoplasma Pneumonia and Bacterial Pneumonia

Honglin Li et al.

Summary: A radiological nomogram combining radiological and clinical characteristics was developed to differentiate mycoplasma pneumonia and bacterial pneumonia effectively, showing good performance in clinical decision-making.

DIAGNOSTICS (2021)

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Article Health Care Sciences & Services

Do as AI say: susceptibility in deployment of clinical decision-aids

Susanne Gaube et al.

Summary: This study found that radiologists rated diagnostic advice as lower quality when it appeared to come from an AI system, while less experienced physicians did not have this bias. Diagnostic accuracy significantly decreased when participants received inaccurate advice, regardless of the purported source being AI or human experts. Therefore, important considerations need to be made when deploying advice in clinical environments, whether it is coming from AI or non-AI sources.

NPJ DIGITAL MEDICINE (2021)

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Article Radiology, Nuclear Medicine & Medical Imaging

Quality of science and reporting of radiomics in oncologic studies: room for improvement according to radiomics quality score and TRIPOD statement

Ji Eun Park et al.

EUROPEAN RADIOLOGY (2020)

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Article Radiology, Nuclear Medicine & Medical Imaging

Exploring Large-scale Public Medical Image Datasets

Luke Oakden-Rayner

ACADEMIC RADIOLOGY (2020)

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Article Radiology, Nuclear Medicine & Medical Imaging

Radiomics of Renal Masses: Systematic Review of Reproducibility and Validation Strategies

Burak Kocak et al.

AMERICAN JOURNAL OF ROENTGENOLOGY (2020)

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Article Biology

Assessing the goodness of fit of logistic regression models in large samples: A modification of the Hosmer-Lemeshow test

Giovanni Nattino et al.

BIOMETRICS (2020)

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Review Radiology, Nuclear Medicine & Medical Imaging

Prostate MRI radiomics: A systematic review and radiomic quality score assessment

Arnaldo Stanzione et al.

EUROPEAN JOURNAL OF RADIOLOGY (2020)

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Editorial Material Radiology, Nuclear Medicine & Medical Imaging

The Long Route to Standardized Radiomics: Unraveling the Knot from the End

Christiane K. Kuhl et al.

RADIOLOGY (2020)

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Article Radiology, Nuclear Medicine & Medical Imaging

The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping

Alex Zwanenburg et al.

RADIOLOGY (2020)

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Review Oncology

How to develop a meaningful radiomic signature for clinical use in oncologic patients

Nikolaos Papanikolaou et al.

CANCER IMAGING (2020)

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Retrospective Motion Correction in Multishot MRI using Generative Adversarial Network

Muhammad Usman et al.

SCIENTIFIC REPORTS (2020)

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Machine Learning in Radiomic Renal Mass Characterization: Fundamentals, Applications, Challenges, and Future Directions

Burak Kocak et al.

AMERICAN JOURNAL OF ROENTGENOLOGY (2020)

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Review Radiology, Nuclear Medicine & Medical Imaging

Radiomics in medical imaging-how-to guide and critical reflection

Janita E. van Timmeren et al.

INSIGHTS INTO IMAGING (2020)

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Article Computer Science, Information Systems

MINIMAR (MINimum Information for Medical Al Reporting): Developing reporting standards for artificial intelligence in health care

Tina Hernandez-Boussard et al.

JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION (2020)

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Radiomics feature reproducibility under inter-rater variability in segmentations of CT images

Christoph Haarburger et al.

SCIENTIFIC REPORTS (2020)

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

Roberto Gatta et al.

EUROPEAN RADIOLOGY EXPERIMENTAL (2020)

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Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension

Samantha Cruz Rivera et al.

LANCET DIGITAL HEALTH (2020)

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Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension

Xiaoxuan Liu et al.

LANCET DIGITAL HEALTH (2020)

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Influence of segmentation margin on machine learning-based high-dimensional quantitative CT texture analysis: a reproducibility study on renal clear cell carcinomas

Burak Kocak et al.

EUROPEAN RADIOLOGY (2019)

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Article Radiology, Nuclear Medicine & Medical Imaging

Reproducibility and non-redundancy of radiomic features extracted from arterial phase CT scans in hepatocellular carcinoma patients: impact of tumor segmentation variability

Qingtao Qiu et al.

QUANTITATIVE IMAGING IN MEDICINE AND SURGERY (2019)

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Radiogenomics: bridging imaging and genomics

Zuhir Bodalal et al.

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Repeatability of Multiparametric Prostate MRI Radiomics Features

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SCIENTIFIC REPORTS (2019)

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Colorectal cancer: Parametric evaluation of morphological, functional and molecular tomographic imaging

Pier Paolo Mainenti et al.

WORLD JOURNAL OF GASTROENTEROLOGY (2019)

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Radiomics with artificial intelligence: a practical guide for beginners

Burak Kocak et al.

DIAGNOSTIC AND INTERVENTIONAL RADIOLOGY (2019)

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Prostate MRI technical parameters standardization: A systematic review on adherence to PI-RADSv2 acquisition protocol

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EUROPEAN JOURNAL OF RADIOLOGY (2019)

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BMC MEDICINE (2019)

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Robustness and Reproducibility of Radiomics in Magnetic Resonance Imaging A Phantom Study

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Reproducibility and Generalizability in Radiomics Modeling: Possible Strategies in Radiologic and Statistical Perspectives

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KOREAN JOURNAL OF RADIOLOGY (2019)

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Repeatability and Reproducibility of Radiomic Features: A Systematic Review

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INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS (2018)

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Reproducibility of F18-FDG PET radiomic features for different cervical tumor segmentation methods, gray-level discretization, and reconstruction algorithms

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CANCER RESEARCH (2017)

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