Quantitative MRI: Defining repeatability, reproducibility and accuracy for prostate cancer imaging biomarker development

Wang, Y.; Tadimalla, Sirisha; Rai, Robba; Goodwin, Jonathan; Foster, Sheryl; Liney, Gary; Holloway, Lois; Haworth, Annette

doi:10.1016/j.mri.2020.12.018

Cited by 29 publications

(34 citation statements)

References 54 publications

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“…Apart from clinically relevant ground truth data, repeatable and reproducible imaging data in large quantities are required to build reliable predictive machine learning models that can be used to monitor response to treatment. Imaging techniques and biomarkers that are found to be technically or biologically unstable should be excluded 189 . Standardization of imaging protocols and data analysis software, along with the reporting of reproducibility and repeatability metrics will enable global validation of biomarkers and translation into standard of care.…”

Section: Discussion and Future Directionsmentioning

confidence: 99%

“…Standardization of protocols is, therefore, crucial to improve the generalisability and clinical application of these imaging biomarkers. Major challenges to overcome include variability in imaging hardware, sequence implementations and inline processing methods across vendors and scanner models, as well as offline post‐processing methods, and models used in different studies 47,87,89,185–189 …”

Section: Practical Challengesmentioning

confidence: 99%

“…As repeatability is dependent on imaging technique, acquisition parameters and hardware, this is ideally measured for each study using consistent study protocols and scanners. Repeated acquisitions in vivo and in phantoms provide estimates of scanner variability and have been included in several studies monitoring treatment response in RT 23,180,188–191 . Longitudinal measurements in phantoms are also valuable in monitoring the stability of qMRI parameters during follow‐up studies and measuring variability between sites and scanners for multi‐centre studies 189 .…”

Section: Practical Challengesmentioning

confidence: 99%

“…Repeated acquisitions in vivo and in phantoms provide estimates of scanner variability and have been included in several studies monitoring treatment response in RT 23,180,188–191 . Longitudinal measurements in phantoms are also valuable in monitoring the stability of qMRI parameters during follow‐up studies and measuring variability between sites and scanners for multi‐centre studies 189 . Ideally, repeatability measurements should be made using the study protocol, however, this may be limited due to financial and time constraints.…”

Section: Practical Challengesmentioning

confidence: 99%

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Artificial intelligence and imaging biomarkers for prostate radiation therapy during and after treatment

et al. 2021

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Magnetic resonance imaging (MRI) is increasingly used in the management of prostate cancer (PCa). Quantitative MRI (qMRI) parameters, derived from multi‐parametric MRI, provide indirect measures of tumour characteristics such as cellularity, angiogenesis and hypoxia. Using Artificial Intelligence (AI), relevant information and patterns can be efficiently identified in these complex data to develop quantitative imaging biomarkers (QIBs) of tumour function and biology. Such QIBs have already demonstrated potential in the diagnosis and staging of PCa. In this review, we explore the role of these QIBs in monitoring treatment response during and after PCa radiotherapy (RT). Recurrence of PCa after RT is not uncommon, and early detection prior to development of metastases provides an opportunity for salvage treatments with curative intent. However, the current method of monitoring treatment response using prostate‐specific antigen levels lacks specificity. QIBs, derived from qMRI and developed using AI techniques, can be used to monitor biological changes post‐RT providing the potential for accurate and early diagnosis of recurrent disease.

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Section: Discussion and Future Directionsmentioning

confidence: 99%

Section: Practical Challengesmentioning

confidence: 99%

Section: Practical Challengesmentioning

confidence: 99%

Section: Practical Challengesmentioning

confidence: 99%

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Artificial intelligence and imaging biomarkers for prostate radiation therapy during and after treatment

et al. 2021

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“…Reproducibility is defined as the “variability in measurements made on the same subject, but under changing conditions” [ 32 ]. The variability and reproducibility are inversely related, i.e., the higher the variability, the lower the reproducibility.…”

Section: Methodsmentioning

confidence: 99%

The Reproducibility of Deep Learning-Based Segmentation of the Prostate Gland and Zones on T2-Weighted MR Images

et al. 2021

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Volume of interest segmentation is an essential step in computer-aided detection and diagnosis (CAD) systems. Deep learning (DL)-based methods provide good performance for prostate segmentation, but little is known about the reproducibility of these methods. In this work, an in-house collected dataset from 244 patients was used to investigate the intra-patient reproducibility of 14 shape features for DL-based segmentation methods of the whole prostate gland (WP), peripheral zone (PZ), and the remaining prostate zones (non-PZ) on T2-weighted (T2W) magnetic resonance (MR) images compared to manual segmentations. The DL-based segmentation was performed using three different convolutional neural networks (CNNs): V-Net, nnU-Net-2D, and nnU-Net-3D. The two-way random, single score intra-class correlation coefficient (ICC) was used to measure the inter-scan reproducibility of each feature for each CNN and the manual segmentation. We found that the reproducibility of the investigated methods is comparable to manual for all CNNs (14/14 features), except for V-Net in PZ (7/14 features). The ICC score for segmentation volume was found to be 0.888, 0.607, 0.819, and 0.903 in PZ; 0.988, 0.967, 0.986, and 0.983 in non-PZ; 0.982, 0.975, 0.973, and 0.984 in WP for manual, V-Net, nnU-Net-2D, and nnU-Net-3D, respectively. The results of this work show the feasibility of embedding DL-based segmentation in CAD systems, based on multiple T2W MR scans of the prostate, which is an important step towards the clinical implementation.

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Bias, Repeatability and Reproducibility of Liver T₁ Mapping With Variable Flip Angles

Tadimalla

Wilson

Shelley

et al. 2022

Magnetic Resonance Imaging

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Quantitative MRI: Defining repeatability, reproducibility and accuracy for prostate cancer imaging biomarker development

Cited by 29 publications

References 54 publications

Artificial intelligence and imaging biomarkers for prostate radiation therapy during and after treatment

Artificial intelligence and imaging biomarkers for prostate radiation therapy during and after treatment

The Reproducibility of Deep Learning-Based Segmentation of the Prostate Gland and Zones on T2-Weighted MR Images

Bias, Repeatability and Reproducibility of Liver T₁ Mapping With Variable Flip Angles

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Quantitative MRI: Defining repeatability, reproducibility and accuracy for prostate cancer imaging biomarker development

Cited by 29 publications

References 54 publications

Artificial intelligence and imaging biomarkers for prostate radiation therapy during and after treatment

Artificial intelligence and imaging biomarkers for prostate radiation therapy during and after treatment

The Reproducibility of Deep Learning-Based Segmentation of the Prostate Gland and Zones on T2-Weighted MR Images

Bias, Repeatability and Reproducibility of Liver T1 Mapping With Variable Flip Angles

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Bias, Repeatability and Reproducibility of Liver T₁ Mapping With Variable Flip Angles