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.

Spatially Resolved Experimental Modal Analysis on High-Speed Composite Rotors Using a Non-Contact, Non-Rotating Sensor.

Due to their lightweight properties, fiber-reinforced composites are well suited for large and fast rotating structures, such as fan blades in turbomachines. To investigate rotor safety and performance, in situ measurements of the structural dynamic behaviour must be performed during rotating conditions. An approach to measuring spatially resolved vibration responses of a rotating structure with a non-contact, non-rotating sensor is investigated here. The resulting spectra can be assigned to specific locations on the structure and have similar properties to the spectra measured with co-rotating sensors, such as strain gauges. The sampling frequency is increased by performing consecutive measurements with a constant excitation function and varying time delays. The method allows for a paradigm shift to unambiguous identification of natural frequencies and mode shapes with arbitrary rotor shapes and excitation functions without the need for co-rotating sensors. Deflection measurements on a glass fiber-reinforced polymer disk were performed with a diffraction grating-based sensor system at 40 measurement points with an uncertainty below 15 µrad and a commercial triangulation sensor at 200 measurement points at surface speeds up to 300 m/s. A rotation-induced increase of two natural frequencies was measured, and their mode shapes were derived at the corresponding rotational speeds. A strain gauge was used for validation.

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.

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.

Development of a Function-Integrative Sleeve for Medical Applications.

Function-integrative textiles bear the potential for a variety of applications in the medical field. Recent clinical investigations suggest that the application of a function-integrative fabric could have a positive impact on currently applied diagnostic procedures of a specific type of tumour. In this context, the fabric should enable local warming of a patient's upper extremity as well as blood flow measurement. Existing solutions comprise a warming a warming system but lack a measuring apparatus for blood flow determination. With regard to the quality of results of current diagnostic procedures, the local warming of the patients' upper extremity and the simultaneous determination of the blood flow plateau are crucial. In the present paper, the development process of a function-integrative sleeve is introduced. Besides the development of an adaptable sleeve-design, the manufacturing process of an integrated warming system was also addressed. Furthermore, the identification of crucial physiological effects, using a Laser Doppler Perfusion Monitor, is introduced. During testing of the function-integrative sleeve, modulation of the desired physiological effects was observed. The results support the initial assumptions and dictate further investigations on increasing user-friendliness and cost-efficiency during adjusting and determining the physiological effects in the course of tumour diagnosis.

Modal Identification of Output-Only Systems of Composite Discs Using Zernike Modes and MAC.

The analysis of the structural dynamic behaviour of composite rotor⁻discs by a valid description of the eigenfrequencies and mode shapes can provide significant information for action-taking before a failure occurs. Specifically, vibration-based diagnostic methods, which are able to take into consideration the interdependencies and sequential changes of the modal properties could benefit from such an analysis. Here, on the example of composite rotors, a correlation method for experimentally determined mode shapes is developed. For this purpose the Zernike polynomials are used to enhance the identification of mode shapes. Furthermore, the modal assurance criterion (MAC) in combination with the frequency response criterion and a data processing approach are applied in order to characterize changing modal properties of composite rotors. In addition, the developed algorithms can be further extended in order to simplify the experimental evaluation of the complex dynamic behaviour of composite structures.

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.

Design and Testing of a Co-Rotating Vibration Excitation System.

A vibration excitation system (VES) in a form of an active coupling is proposed, designed and manufactured. The system is equipped with a set of piezoelectric stack actuators uniformly distributed around the rotor axis and positioned parallel to each other. The actuator arrangement allows an axial displacement of the coupling halves as well as their rotation about any transverse axis. Through the application of the VES an aimed vibration excitation is realised in a co-rotating coordinate system, which enables a non-invasive and precise modal analysis of rotating components. As an example, the VES is applied for the characterisation of the structural dynamic behaviour of a generic steel rotor at different rotational speeds. The first results are promising for both stationary and rotating conditions.