Prediction of metastatic pheochromocytoma and paraganglioma: a machine learning modelling study using data from a cross-sectional cohort

BACKGROUND

Pheochromocytomas and paragangliomas have up to a 20% rate of metastatic disease that cannot be reliably predicted. This study prospectively assessed whether the dopamine metabolite, methoxytyramine, might predict metastatic disease, whether predictions might be improved using machine learning models that incorporate other features, and how machine learning-based predictions compare with predictions made by specialists in the field.

METHODS

In this machine learning modelling study, we used cross-sectional cohort data from the PMT trial, based in Germany, Poland, and the Netherlands, to prospectively examine the utility of methoxytyramine to predict metastatic disease in 267 patients with pheochromocytoma or paraganglioma and positive biochemical test results at initial screening. Another retrospective dataset of 493 patients with these tumors enrolled under clinical protocols at National Institutes of Health (00-CH-0093) and the Netherlands (PRESCRIPT trial) was used to train and validate machine learning models according to selections of additional features. The best performing machine learning models were then externally validated using data for all patients in the PMT trial. For comparison, 12 specialists provided predictions of metastatic disease using data from the training and external validation datasets.

FINDINGS

Prospective predictions indicated that plasma methoxytyramine could identify metastatic disease at sensitivities of 52% and specificities of 85%. The best performing machine learning model was based on an ensemble tree classifier algorithm that used nine features: plasma methoxytyramine, metanephrine, normetanephrine, age, sex, previous history of pheochromocytoma or paraganglioma, location and size of primary tumours, and presence of multifocal disease. This model had an area under the receiver operating characteristic curve of 0·942 (95% CI 0·894-0·969) that was larger (p<0·0001) than that of the best performing specialist before (0·815, 0·778-0·853) and after (0·812, 0·781-0·854) provision of SDHB variant data. Sensitivity for prediction of metastatic disease in the external validation cohort reached 83% at a specificity of 92%.

INTERPRETATION

Although methoxytyramine has some utility for prediction of metastatic pheochromocytomas and paragangliomas, sensitivity is limited. Predictive value is considerably enhanced with machine learning models that incorporate our nine recommended features. Our final model provides a preoperative approach to predict metastases in patients with pheochromocytomas and paragangliomas, and thereby guide individualised patient management and follow-up.

FUNDING

Deutsche Forschungsgemeinschaft.

Structural Damage Identification of Composite Rotors Based on Fully Connected Neural Networks and Convolutional Neural Networks

Damage identification of composite structures is a major ongoing challenge for a secure operational life-cycle due to the complex, gradual damage behaviour of composite materials. Especially for composite rotors in aero-engines and wind-turbines, a cost-intensive maintenance service has to be performed in order to avoid critical failure. A major advantage of composite structures is that they are able to safely operate after damage initiation and under ongoing damage propagation. Therefore, a robust, efficient diagnostic damage identification method would allow monitoring the damage process with intervention occurring only when necessary. This study investigates the structural vibration response of composite rotors by applying machine learning methods and the ability to identify, localise and quantify the present damage. To this end, multiple fully connected neural networks and convolutional neural networks were trained on vibration response spectra from damaged composite rotors with barely visible damage, mostly matrix cracks and local delaminations using dimensionality reduction and data augmentation. A databank containing 720 simulated test cases with different damage states is used as a basis for the generation of multiple data sets. The trained models are tested using k-fold cross validation and they are evaluated based on the sensitivity, specificity and accuracy. Convolutional neural networks perform slightly better providing a performance accuracy of up to 99.3% for the damage localisation and quantification.

Non-destructive testing of a rotating glass-fibre-reinforced polymer disc by swept source optical coherence tomography

Composite materials are used for high-performance rotating blades, e.g. in turbines and wind power plants. Here, optical coherence tomography was used to visualize large areas of fibre-reinforcedpolymer discs at rotation speeds of up to 1200 rpm. These measurements allowed to visualize the fibre structure over large areas of the disc. By recording the front and back reflex, the wobble of the disc was measured precisely. Additionally, the recorded structure was used to detect even small deviations from a uniform rotation.

Diffraction-grating-based in situ displacement, tilt, and strain measurements on high-speed composite rotors

Polymer composite rotors offer promising perspectives in high-speed applications such as turbomachinery. However, failure modeling is a challenge due to the material's anisotropy and heterogeneity, which makes high-speed in situ deformation measurements necessary. The challenge is to maintain precision and accuracy in the environment of fast rigid-body movement. A diffraction-grating-based sensor is used for spatio-temporally resolved displacement, tilt, and strain measurements at surface velocities up to 260 m/s with statistical strain uncertainties down to $16\,\,\unicode{x00B5}{\epsilon}$. As a line camera is used, vibrations in the kHz range are measurable in principle. Due to sensor calibration and the use of a novel scan-correlation analysis approach, the rigid-body-movement-induced uncertainties are reduced significantly. The measurement of strain fluctuations on a rotating composite disc show that the crack propagation can be tracked spatially resolved and as a function of the rotational speed, which makes an in situ quantification of the damage state of the rotor possible.

A Sequence-Based Damage Identification Method for Composite Rotors by Applying the Kullback-Leibler Divergence, a Two-Sample Kolmogorov-Smirnov Test and a Statistical Hidden Markov Model

Composite structures undergo a gradual damage evolution from initial inter-fibre cracks to extended damage up to failure. However, most composites could remain in service despite the existence of damage. Prerequisite for a service extension is a reliable and component-specific damage identification. Therefore, a vibration-based damage identification method is presented that takes into consideration the gradual damage behaviour and the resulting changes of the structural dynamic behaviour of composite rotors. These changes are transformed into a sequence of distinct states and used as an input database for three diagnostic models, based on the Kullback–Leibler divergence, the two-sample Kolmogorov–Smirnov test and a statistical hidden Markov model. To identify the present damage state based on the damage-dependent modal properties, a sequence-based diagnostic system has been developed, which estimates the similarity between the present unclassified sequence and obtained sequences of damage-dependent vibration responses. The diagnostic performance evaluation delivers promising results for the further development of the proposed diagnostic method.

Influence of Gradual Damage on the Structural Dynamic Behaviour of Composite Rotors: Experimental Investigations

Fibre-reinforced composite structures subjected to complex loads exhibit gradual damage behaviour with the degradation of the effective mechanical properties and changes in their structural dynamic behaviour. Damage manifests itself as a spatial increase in inter-fibre failure and delamination growth, resulting in local changes in stiffness. These changes affect not only the residual strength but, more importantly, the structural dynamic behaviour. In the case of composite rotors, this can lead to catastrophic failure if an eigenfrequency coincides with the rotational speed. The description and analysis of the gradual damage behaviour of composite rotors, therefore, provide the fundamentals for a better understanding of unpredicted structural phenomena. The gradual damage behaviour of the example composite rotors and the resulting damage-dependent dynamic behaviour were experimentally investigated under propagating damage caused by a combination of out-of-plane and in-plane loads. A novel observation is the finding that a monotonic increase in damage results in a non-monotonic frequency shift of a significant number of eigenfrequencies.