1. Development and evaluation of machine-learning methods in whole-body magnetic resonance imaging with diffusion weighted imaging for staging of patients with cancer: the MALIBO diagnostic test accuracy study
- Author
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Andrea Rockall, Xingfeng Li, Nicholas Johnson, Ioannis Lavdas, Shalini Santhakumaran, A Toby Prevost, Dow-Mu Koh, Shonit Punwani, Vicky Goh, Nishat Bharwani, Amandeep Sandhu, Harbir Sidhu, Andrew Plumb, James Burn, Aisling Fagan, Alf Oliver, Georg J Wengert, Daniel Rueckert, Eric Aboagye, Stuart A Taylor, and Ben Glocker
- Subjects
whole body mri ,machine learning ,lesion detection ,mri image segmentation ,sensitivity and specificity ,cancer staging ,diffusion weighted imaging ,diagnostic test accuracy study ,Medicine - Abstract
Background Whole-body magnetic resonance imaging is accurate, efficient and cost-effective for cancer staging. Machine learning may support radiologists reading whole-body magnetic resonance imaging. Objectives To develop a machine-learning algorithm to detect normal organs and cancer lesions. To compare diagnostic accuracy, time and agreement of radiology reads to detect metastases using whole-body magnetic resonance imaging with concurrent machine learning (whole-body magnetic resonance imaging + machine learning) against standard whole-body magnetic resonance imaging (whole-body magnetic resonance imaging + standard deviation). Design and participants Retrospective analysis of (1) prospective single-centre study in healthy volunteers > 18 years (n = 51) and (2) prospective multicentre STREAMLINE study patient data (n = 438). Tests Index: whole-body magnetic resonance imaging + machine learning. Comparator: whole-body magnetic resonance imaging + standard deviation. Reference standard Previously established expert panel consensus reference at 12 months from diagnosis. Outcome measures Primary: difference in per-patient specificity between whole-body magnetic resonance imaging + machine learning and whole-body magnetic resonance imaging + standard deviation. Secondary: per-patient sensitivity, per-lesion sensitivity and specificity, read time and agreement. Methods Phase 1: classification forests, convolutional neural networks, and a multi-atlas approaches for organ segmentation. Phase 2/3: whole-body magnetic resonance imaging scans were allocated to Phase 2 (training = 226, validation = 45) and Phase 3 (testing = 193). Disease sites were manually labelled. The final algorithm was applied to 193 Phase 3 cases, generating probability heatmaps. Twenty-five radiologists (18 experienced, 7 inexperienced in whole-body magnetic resonance imaging) were randomly allocated whole-body magnetic resonance imaging + machine learning or whole-body magnetic resonance imaging + standard deviation over two or three rounds in a National Health Service setting. Read time was independently recorded. Results Phases 1 and 2: convolutional neural network had best Dice similarity coefficient, recall and precision measurements for healthy organ segmentation. Final algorithm used a ‘two-stage’ initial organ identification followed by lesion detection. Phase 3: evaluable scans (188/193, of which 50 had metastases from 117 colon, 71 lung cancer cases) were read between November 2019 and March 2020. For experienced readers, per-patient specificity for detection of metastases was 86.2% (whole-body magnetic resonance imaging + machine learning) and 87.7% (whole-body magnetic resonance imaging + standard deviation), (difference −1.5%, 95% confidence interval −6.4% to 3.5%; p = 0.387); per-patient sensitivity was 66.0% (whole-body magnetic resonance imaging + machine learning) and 70.0% (whole-body magnetic resonance imaging + standard deviation) (difference −4.0%, 95% confidence interval −13.5% to 5.5%; p = 0.344). For inexperienced readers (53 reads, 15 with metastases), per-patient specificity was 76.3% in both groups with sensitivities of 73.3% (whole-body magnetic resonance imaging + machine learning) and 60.0% (whole-body magnetic resonance imaging + standard deviation). Per-site specificity remained high within all sites; above 95% (experienced) or 90% (inexperienced). Per-site sensitivity was highly variable due to low number of lesions in each site. Reading time lowered under machine learning by 6.2% (95% confidence interval −22.8% to 10.0%). Read time was primarily influenced by read round with round 2 read times reduced by 32% (95% confidence interval 20.8% to 42.8%) overall with subsequent regression analysis showing a significant effect (p = 0.0281) by using machine learning in round 2 estimated as 286 seconds (or 11%) quicker. Interobserver variance for experienced readers suggests moderate agreement, Cohen’s κ = 0.64, 95% confidence interval 0.47 to 0.81 (whole-body magnetic resonance imaging + machine learning) and Cohen’s κ = 0.66, 95% confidence interval 0.47 to 0.81 (whole-body magnetic resonance imaging + standard deviation). Limitations Patient whole-body magnetic resonance imaging data were heterogeneous with relatively few metastatic lesions in a wide variety of locations, making training and testing difficult and hampering evaluation of sensitivity. Conclusions There was no difference in diagnostic accuracy for whole-body magnetic resonance imaging radiology reads with or without machine-learning support, although radiology read time may be slightly shortened using whole-body magnetic resonance imaging + machine learning. Future work Failure-case analysis to improve model training, automate lesion segmentation and transfer of machine-learning techniques to other tumour types and imaging modalities. Study registration This study is registered as ISRCTN23068310. Funding This award was funded by the National Institute for Health and Care Research (NIHR) Efficacy and Mechanism Evaluation (EME) programme (NIHR award ref: 13/122/01) and is published in full in Efficacy and Mechanism Evaluation; Vol. 11, No. 15. See the NIHR Funding and Awards website for further award information. Plain language summary Whole-body magnetic resonance imaging demonstrates the entire body and can detect the spread of tumour, without the burden of ionising radiation. Recently, the STREAMLINE study reported that whole-body magnetic resonance imaging is accurate, efficient and cost-effective for cancer staging. However, whole-body magnetic resonance imaging is complex to report. Machine learning is a type of artificial intelligence whereby a computer learns from being given previous data to undertake a task, using techniques such as classification forests, convolutional neural networks, and multi-atlas approaches. Our aim was to develop a machine-learning method to automatically detect lesions on whole-body magnetic resonance imaging to support radiologists by potentially improving their ability to correctly detect disease and reduce the reading time of whole-body magnetic resonance imaging scans in patients with cancer. Firstly, whole-body magnetic resonance imaging scans from 51 healthy volunteers were used to develop machine-learning methods to automatically detect normal organs. Secondly, machine-learning methods were trained to detect cancer lesions, using 271 whole-body magnetic resonance imaging scans from a previous study. Finally, the refined machine-learning technique was tested in 188 patient scans from a previous study, to see if the technique could improve radiology reporting by increasing accuracy and speed in detecting disease. We designed a system to test the accuracy of radiologists reading whole-body magnetic resonance imaging with or without machine-learning support in a near-real clinical National Health Service setting. Twenty-five independent radiologists (18 experienced in reading whole-body magnetic resonance imaging and 7 radiologists inexperienced in whole-body magnetic resonance imaging) were randomly allocated whole-body magnetic resonance imaging scans to read with or without machine-learning support. We found that machine-learning support resulted in similar accuracy for detecting disease and was slightly more efficient in the reading time than for radiological reads without machine-learning support. Differences in interpretation between experienced readers were considered moderate in both cases. Overall, the study was an ambitious attempt to undertake a highly complex machine-learning task, to detect cancer on whole-body magnetic resonance imaging. Many important steps have been taken but the current machine-learning algorithm did not result in a significant improvement in the radiologist’s accuracy for disease detection, although it may have slightly reduced the time taken to read the study. Future work is advocated to further develop machine-learning tools to improve the accuracy of tumour detection. Scientific summary Background Whole-body magnetic resonance imaging (WB-MRI) has been developed in the last decade and it has been proposed as an alternative to multimodality cancer staging pathways. The STREAMLINE study has demonstrated that staging of lung and colon cancer via WB-MRI was of similar accuracy to standard care staging pathways but resulted in fewer tests being required, and a reduction in staging time and cost. However, this technique has not widely translated into clinical practice, being limited to a few expert centres. A barrier to translation may be the complexity of interpretation by inexperienced readers. A machine-learning (ML) algorithm for automated detection of cancer lesions, to assist radiologists, may allow clinical translation of WB-MRI for the benefit of patient care. Objective The objective of Phases 1 and 2 was to develop a ML method for automatic detection of cancer lesions on WB-MRI. The primary objective of Phase 3 was to compare the diagnostic test accuracy of WB-MRI in patients being staged for cancer with and without ML support, when read by independent, experienced readers. The reference standard was the consensus reference panel from the STREAMLINE study, which had recorded the sites of disease in the STREAMLINE patients using all available clinical information for a 12-month follow-up from time of enrolment. The planned secondary objectives of this study were: to compare the reading time of WB-MRI scans, with and without ML support; to determine the interobserver variability of WB-MRI diagnosis by different radiologists, with and without ML support; to evaluate the diagnostic accuracy of WB-MRI read by non-experienced readers, with and without ML support; to evaluate different combinations of acquired MRI sequences; all the above with and without ML support (not achieved); to determine any difference in costs related to radiology reading time by means of a simple cost-effectiveness analysis (not achieved). Design This was an observational study (study limited to working with data), using different patient cohorts, and methodologies, being evaluated in series during three consecutive phases. Phase 1: Previously acquired WB-MRI scans in 51 healthy volunteers were stitched into imaging volumes. The normal organs and skeleton were segmented by a trained radiologist. We compared and tested several state-of-the-art medical image segmentation methods to train an algorithm to automatically detect and segment the organs. This included a multi-atlas (MA) approach, classification forests (CFs) and convolutional neural networks (CNNs) methods. For the CFs method, we used 50 trees with a maximum tree depth of 30 in the segmentation. The stopping criterion for growing trees was if either the objective function (information gain) could not be further improved or the number of training samples in a leaf fell below a threshold of four samples. For the CNNs method, we employed a dual pathway (2 resolutions), 11-layer deep CNN, where the last 2 layers correspond to fully connected layers, which combine the features extracted on the 2 resolution pathways. We adopted 50–70 feature maps (different kernels) for each layer. The network architecture was fully convolutional and there were no max-pooling layers, which we found to increase segmentation accuracy. The CNN architecture was a balance between model capacity, training efficiency, and memory demands. The third algorithm was based on a MA label propagation approach. MA segmentation uses a set of atlases (images with corresponding segmentations) that represent the interparticipant variability of the anatomy to be segmented. Each atlas was registered to the new image to be segmented using a deformable image registration. The MA approach accounts for anatomical shape variability and is more robust than single-atlas propagation methods, in that at any errors associated with propagation were averaged out when combining multiple atlases. The approach employed here makes use of efficient 3D–3D intensity-based image registration with free-form deformations as the transformation model and correlation coefficient as the similarity measure. Majority voting is used to derive the final tissue label at each voxel. Phase 2: Available WB-MRI scans from the STREAMLINE study (n = 438) were allocated by the study statistician to Phase 2 (model training) and clinical validation (hold-out set of 193 cases). The visible lesions in cases allocated to Phase 2 were segmented by trained radiologists, using the defined consensus reference standard for the sites of disease. Using 226 evaluable and annotated cases, we trained a model for lesion detection and localisation rather than attempting accurate automated segmentation. We found that lesion detection in WB-MRI was suboptimal with the CNN alone. An optimal ‘two-stage’ ML method was developed and tested. In the first stage of the ‘two-stage’ method, the information from Phase 1 was used to identify the position of organs and bones and in stage two, the lesions were detected. Stage two could be modular with respect to the anatomical location in which the suspected lesion could be found. The architecture and configuration of the used CNN were modified to achieve optimal performance. Phase 3: The final two-stage ML algorithm from Phase 2 was applied to the 193 cases that were held out for Phase 3, generating probability heatmap volumes. WB-MRI scans were reported by 25 independent radiologists (18 radiologists experienced in reading WB-MRI and 7 radiologists inexperienced in WB-MRI). All radiologists took part in the 1st and 2nd round reads which incorporated inter-rater reads, and 8 participated in the 3rd round read for intrarater reads. All reads were undertaken in an NHS radiology reporting room, although using a separate cloud-based picture archiving and communication system (PACS). Based on experience level, 10–16 cases were randomly allocated by the study statistician to the reading lists for inexperienced or experienced radiologists. Each radiologist read both lung and colon cases with and without ML as a paired cohort. There were at least 4 weeks interval between each reading round for individual radiologists. Cases allocated to round 1 would be a random mixture of cases with and without ML support and then in round 2 the cases would be reversed such that each case was read once with and once without ML support. The number of cases with and without ML output was balanced in each reading round. A scribe recorded reading time. Results Phase 1 results We found that CNNs outperformed CFs and the MA algorithm when T2w volumes were used as input to the algorithms and when using pooled overlap-evaluation metrics [Dice similarity coefficient (DSC), recall (RE), precision (PR)] to assess the accuracy of segmentation. When the performance of the algorithms was assessed, with pooled surface distance metrics [average surface distance (ASD), root-mean-square surface distance (RMSSD), and Hausdorff distance (HD)], it was the MA algorithm that performed best. Single misinterpreted voxels in CFs and CNNs can greatly elevate ASD, RMSSD, and HD; these metrics are particularly sensitive to outliers. We then assessed the pooled metrics performance of CFs and CNNs when using all imaging combinations [T2w + T1w + diffusion-weighted imaging (DWI)] as input, arguing that maximisation of training information to the algorithms might improve the performance of segmentation. We found that the performance of CFs was improved, however not significantly, when using all imaging combinations as input for training. The opposite was observed for CNNs. The findings for the pooled metrics analysis, described above, were corroborated by a ‘per-organ’ quantitative analysis of the commonly used DSC, to assess the performance of our segmentation algorithms. This analysis confirmed that for all individual anatomical structures (except for the bladder), the algorithm that returned the greatest DSC was CNNs with T2w images only used as input. As our morphological T2w and T1w scans were acquired using breath-holds and the DWI sequence was acquired with free breathing, we found that there was significant displacement between soft tissues in anatomical areas adjacent to the diaphragm between these types of scans. As the employed affine registration method could not fully compensate for nonlinear motions caused by breathing, we assumed that misregistration could be the reason why the performance of CNNs, despite performing better than the other two algorithms when using T2w volumes as input only, was degraded when using all imaging combinations as input for training. A more robust, nonlinear registration method could improve the accuracy of CNNs and further improve the performance of CFs. The performance of our methods cannot be directly compared to similar methods in the literature because there is no previous work describing automatic, simultaneous segmentation of healthy organs and bones in multiparametric WB-MRI. We believe, however, that our methods may compare favourably to other ML methods for detection and segmentation in medical imaging because our classifiers are inherently multilabel and effective training was achieved when using a relatively small number of data sets, something that is very important in the clinical setting. However, we still need to address the performance limitations of our algorithms when segmenting organs with big variability in appearance (e.g. the gallbladder or the pancreas). Phase 2 results We tested different ML methods for lesion detection. We found that using CNNs was not optimal for lesion detection on WB-MRI. This may have been due to the small fraction of lesion volume occupying the scanned space, when compared to the WB volume. It may also have been due to the complexity of intensities in background tissue and the lesion with weak boundaries causing challenges for the CNNs. We, therefore, adapted our process to become an optimal two-stage process, with an initial stage for detection of organs as per the Phase 1 technique followed by a CNN to detect lesions. Stage two could be modular with respect to the anatomical location where the suspected lesion can be found. The architecture and configuration of the used CNN could be modified to achieve optimal performance for lesion detection. Phase 3 results All radiology reads were completed between November 2019 and March 2020. Among the 193 cases allocated to Phase 3, 188 WB-MRI scans were evaluable (117 colon and 71 lung cancer) and 50/188 cases had metastases. Per-patient specificity for detection of metastases within experienced readers was 86.2% (WB-MRI + ML) and 87.7% [WB-MRI + standard deviation (SD)], [difference −1.5%, 95% confidence interval (CI) −6.4%, 3.5%; p = 0.387]. Per-patient sensitivity produced results of 66.0% (WB-MRI + ML) and 70.0% (WB-MRI + SD) (difference −4.0%, 95% CI −13.5% to 5.5%; p = 0.344). For inexperienced readers (53 reads, 15 with metastases), per-patient specificity was 76.3% in both groups with sensitivities of 73.3% (WB-MRI + ML) and 60.0% (WB-MRI + SD). Per-site specificity remained high within all sites; above 95% (experienced) or 90% (inexperienced). Per-site sensitivity was highly variable due to the low number of lesions in each site, hampering interpretation. Reading time was lowered under ML by 6.2% (95% CI −22.8% to 10.0%). Read time was primarily influenced by read round with round 2 read times reduced by 32% (95% CI 20.8% to 42.8%) overall with subsequent regression analysis showing a significant effect (p = 0.0281) by using ML in round 2 estimated as 286 seconds (or 11%) quicker. Interobserver variance for experienced readers suggests moderate agreement, Cohen’s κ = 0.64, 95% CI 0.47 to 0.81 (WB-MRI + ML) and Cohen’s κ = 0.66, 95% CI 0.47 to 0.81 (WB-MRI + SD). Conclusion Phase 1 In Phase 1, we developed and evaluated three state-of-the-art algorithms that automatically segment healthy organs and bones in WB-MRI with accuracy comparable to the one achieved manually by clinical experts, using relatively sparse training data. An algorithm based on CNNs and trained using T2w-only images as input performs favourably when compared to CFs or a MA algorithm, trained with either T2w-only images or a combination of imaging inputs (T2w + T1w + DWI). Using multimodal MRI data as input for training did not improve the segmentation performance in this work, but it is anticipated to improve the segmentation performance if more effective WB registration between the various imaging modalities can be performed. This investigation was the first step towards developing robust algorithms for the automatic detection and segmentation of benign and malignant lesions in WB-MRI scans for staging of cancer patients. Phase 2 There were many challenges in training the algorithm for lesion detection, with a very heterogeneous data set, acquired at 16 sites on different machines. The low number of metastatic lesions were scattered through many anatomic sites. Identification of lesions of relatively small volume in relation to the large volume of the WB-MRI required a two-stage approach for lesion detection, with initial organ identification followed by lesion detection. Phase 3 We undertook a robust diagnostic test accuracy study comparing WB-MRI with ML support (index text) with standard WB-MRI, without ML support (comparator test), with paired reads separated by a wash-out period. Although there was no clear statistical difference with or without ML support in terms of diagnostic test accuracy, either in experienced or inexperienced hands, we found that ML reads were likely to be a little shorter in reading time. Implications for health care Machine learning support for image interpretation is a rapidly expanding area of research. With continually increasing demand for diagnostic imaging and a radiology workforce crisis in the NHS, as well as globally, ML techniques may offer support to allow complex imaging modalities, such as WB-MRI, to be ready for translation into clinical care for the benefit of patients. Phase 1 demonstrated that with relatively sparse data, we could develop very successful organ segmentation tools in highly complex data sets and this has many potential future roles. In this study, we used this as a first step prior to lesion detection and this generic method could be applied to a wider range of disease areas, including other tumour types. We found that ML support slightly shorted the reading time, although with no improvement in diagnostic accuracy. However, we have shown that lesion detection using ML is achievable on heterogeneous and complex multiparametric WB-MRI scans. With further model training, we believe that ML support is feasible in the future, with the potential to improve translation of WB-MRI more widely. In addition, many of the techniques that have been developed in the course of the study have the potential to be applied to other areas of diagnostic imaging. Recommendations for future research Future research should investigate: Further improvement in lesion detection accuracy, with evaluation of failure cases for improved model training. Evaluate lesion detection technique on other cancers, notably breast, prostate and myeloma. Further developments in understanding the marrow composition in healthy aging and in metastatic cancer. Developing further understanding and methods to overcome registration challenges between breath-hold and non-breath-hold MRI sequences in order to improve ML tissue characterisation. Study registration This study is registered as ISRCTN23068310. Funding This award was funded by the National Institute for Health and Care Research (NIHR) Efficacy and Mechanism Evaluation (EME) programme (NIHR award ref: 13/122/01) and is published in full in Efficacy and Mechanism Evaluation; Vol. 11, No. 15. See the NIHR Funding and Awards website for further award information.
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- 2024
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