Longitudinal clustering analysis and prediction of Parkinson\u27s disease progression using radiomics and hybrid machine learning

Background: We employed machine learning approaches to (I) determine distinct progression trajectories in Parkinson\u27s disease (PD) (unsupervised clustering task), and (II) predict progression trajectories (supervised prediction task), from early (years 0 and 1) data, making use of clinical and imaging features. Methods: We studied PD-subjects derived from longitudinal datasets (years 0, 1, 2 & 4; Parkinson\u27s Progressive Marker Initiative). We extracted and analyzed 981 features, including motor, non-motor, and radiomics features extracted for each region-of-interest (ROIs: left/right caudate and putamen) using our standardized standardized environment for radiomics analysis (SERA) radiomics software. Segmentation of ROIs on dopamine transposer - single photon emission computed tomography (DAT SPECT) images were performed via magnetic resonance images (MRI). After performing cross-sectional clustering on 885 subjects (original dataset) to identify disease subtypes, we identified optimal longitudinal trajectories using hybrid machine learning systems (HMLS), including principal component analysis (PCA) + K-Means algorithms (KMA) followed by Bayesian information criterion (BIC), Calinski-Harabatz criterion (CHC), and elbow criterion (EC). Subsequently, prediction of the identified trajectories from early year data was performed using multiple HMLSs including 16 Dimension Reduction Algorithms (DRA) and 10 classification algorithms. Results: We identified 3 distinct progression trajectories. Hotelling\u27s t squared test (HTST) showed that the identified trajectories were distinct. The trajectories included those with (I, II) disease escalation (2 trajectories, 27% and 38% of patients) and (III) stable disease (1 trajectory, 35% of patients). For trajectory prediction from early year data, HMLSs including the stochastic neighbor embedding algorithm (SNEA, as a DRA) as well as locally linear embedding algorithm (LLEA, as a DRA), linked with the new probabilistic neural network classifier (NPNNC, as a classifier), resulted in accuracies of 78.4% and 79.2% respectively, while other HMLSs such as SNEA + Lib_SVM (library for support vector machines) and t_SNE (t-distributed stochastic neighbor embedding) + NPNNC resulted in 76.5% and 76.1% respectively. Conclusions: This study moves beyond cross-sectional PD subtyping to clustering of longitudinal disease trajectories. We conclude that combining medical information with SPECT-based radiomics features, and optimal utilization of HMLSs, can identify distinct disease trajectories in PD patients, and enable effective prediction of disease trajectories from early year data

Robust identification of Parkinson\u27s disease subtypes using radiomics and hybrid machine learning

OBJECTIVES: It is important to subdivide Parkinson\u27s disease (PD) into subtypes, enabling potentially earlier disease recognition and tailored treatment strategies. We aimed to identify reproducible PD subtypes robust to variations in the number of patients and features. METHODS: We applied multiple feature-reduction and cluster-analysis methods to cross-sectional and timeless data, extracted from longitudinal datasets (years 0, 1, 2 & 4; Parkinson\u27s Progressive Marker Initiative; 885 PD/163 healthy-control visits; 35 datasets with combinations of non-imaging, conventional-imaging, and radiomics features from DAT-SPECT images). Hybrid machine-learning systems were constructed invoking 16 feature-reduction algorithms, 8 clustering algorithms, and 16 classifiers (C-index clustering evaluation used on each trajectory). We subsequently performed: i) identification of optimal subtypes, ii) multiple independent tests to assess reproducibility, iii) further confirmation by a statistical approach, iv) test of reproducibility to the size of the samples. RESULTS: When using no radiomics features, the clusters were not robust to variations in features, whereas, utilizing radiomics information enabled consistent generation of clusters through ensemble analysis of trajectories. We arrived at 3 distinct subtypes, confirmed using the training and testing process of k-means, as well as Hotelling\u27s T2 test. The 3 identified PD subtypes were 1) mild; 2) intermediate; and 3) severe, especially in terms of dopaminergic deficit (imaging), with some escalating motor and non-motor manifestations. CONCLUSION: Appropriate hybrid systems and independent statistical tests enable robust identification of 3 distinct PD subtypes. This was assisted by utilizing radiomics features from SPECT images (segmented using MRI). The PD subtypes provided were robust to the number of the subjects, and features

The Effect of Scattering from Leg Region on Organ Doses in Prostate Brachytherapy for 103Pd, 125I and 131Cs Seeds

Introduction Dose calculation of tumor and surrounding tissues is essential during prostate brachytherapy. Three radioisotopes, namely, 125I, 103Pd, and 131Cs, are extensively used in this method. In this study, we aimed to calculate the received doses by the prostate and critical organs using the aforementioned radioactive seeds and to investigate the effect of scattering contribution for the legs on dose calculations. Materials and Methods The doses to organs of interest were calculated using MCNPX code and ORNL (Oak Ridge National Laboratory) phantom. Results Doses to the prostate as a source of radiation for 125I, 103Pd, and 131Cs were approximately 108.9, 97.7, and 81.5 Gy, respectively. Bladder, sigmoid colon, and testes received higher doses than other organs due to proximity to the prostate. Differences between the doses when tallying with the legs intact and with the legs voided were significant for testes, sigmoid colon contents, and sigmoid colon wall because of their proximity to the prostate. There was also a good consistency between our results and the data published by Montefiore Medical Center and Albert Einstein College of Medicine for the prostate. Conclusion Scattering from leg region had a significant effect on doses to testes, sigmoid colon contents, and sigmoid colon wall in the pelvic region, and prostate and the other organs were unaffected. Brachytherapy treatment plans using 131Cs seeds allow for better sparing of critical tissues, with a comparable number of, or fewer, seeds required, compared to 125I seeds

Energy Optimization And Calculation Of Dose Absorption Enhancement Factor In Photon Activation Therapy

Introduction: Secondary radiation such as photoelectrons, Auger electrons and characteristic radiations cause a local boost in dose for a tumor when irradiated with an external X-ray beam after being loaded with elements capable of activating the tumor, e.g.; I and Gd. Materials and Methods:  In this investigation, the MCNPX code was used for simulation and calculation of dose enhancement factor for a tumor loaded with activating elements. The designed model comprised the X-ray source, phantom (target tissue and loaded tumor with activating agent), detector, interactions modeling and results. The source was defined as monochromatic and plane surface situated at 50 cm (z = 50). Phantom geometry was a 10 × 10 × 10 cm3 cube centered at (0, 0, 0) with a 2.2 × 2.2 × 2.2 cm3 cubic tumor with a center located at 3 cm depth inside the phantom Results: Dose enhancement factor and optimum energy in radiotherapy are evaluated using the photon activation therapy method. Result show that the dose enhancement factor increases with activating concentration in the tumor. The maximum dose enhancement factor for iodine in the tumor occurs for photons in the energy range of 50-60 keV. Dose uniformity is less for lower energy photons within the activated region inside the tumor. Results indicate that the dose enhancement factor varies linearly with the activating concentration agent. Discussion and Conclusion: In this study, the obtained results point out a considerable enhancement in dose in the presence of activating agents in the tumor regions

Porosity Detection of Laser Based Additive Manufacturing Using Melt Pool Morphology Clustering

The microstructural and mechanical properties of Laser Based Additive Manufacturing (LBAM) are still inconsistent and unreliable, which is a major barrier that prevents Additive Manufacturing (AM) from entering main stream production. The key challenge is the lack of understanding for the underlying process-properties relationship. We monitor Laser Engineered Net Shaped (LENS) process using a state-of-art thermal image system, and the resulting high-speed Melt Pool (MP) data stream is used to characterize the complex thermo-physical process. We propose a novel method based on Self-Organizing Map to cluster the MPs based on their morphology and link MPs clusters’ characteristics to the porosity of fabricated parts, which is crucial to mechanical properties of parts. The results are validated using X-Ray tomography of Ti-64 thin-wall. Our approach identifies various patterns of MP morphology, which corresponds to different types of porosities. The proposed method can potentially be used to certify the part quality in a real-time and non-destructive manner.Mechanical Engineerin

Production, biodistribution, and dosimetry of 47Sc-1,4,7,10-tetraazacyclododecane-1,4,7,10-tetramethylene phosphonic acid as a bone-seeking radiopharmaceutical

In this study 1,4,7,10-tetraazacyclododecane-1,4,7,10-tetramethylene phosphonic acid (DOTMP) was used as the polyaminophosphonic acid carrier ligand and the therapeutic potential of the bone seeking radiopharmaceutical 47Sc-DOTMP was assessed by measuring its dosage–dependent skeletal uptake and then the absorbed radiation dose of human organs was estimated. Because of limited availability of 47Sc we performed some preliminary studies using 46Sc. 46Sc was produced with a specific activity of 116.58 MBq/mg (3.15 mCi/mg) and radionuclide purity of 98%. 46Sc-DOTMP was prepared and an activity of 1.258 MBq (34 μCi) at a chelant-to-metal ratio of 60:1 was administered to five groups of mice with each group containing 3 mice that were euthanized at 4, 24, 48, 96 and 192 h post administration. The heart, lungs, liver, spleen, kidneys, intestine, skin, muscle, and a femur were excised, weighed, and counted. The data were analyzed to determine skeletal uptake and source organ residence times and cumulated activities for 47Sc-DOTMP. 46Sc-DOTMP complex was prepared in radiochemical purity about 93%. In vitro stability of complex was evaluated at room temperature for 48 h. Biodistribution studies of complex in mice were studied for 7 days. The data were analyzed to estimate skeletal uptake and absorbed radiation dose of human organs using biodistribution data from mice. By considering the results, 47Sc-DOTMP is a possible therapeutic agent for using in palliation of bone pain due to metastatic skeletal lesions from several types of primary cancers in prostate, breast, etc

Comparison and Evaluation of the Effects of Rib and Lung Inhomogeneities on Lung Dose in Breast Brachytherapy using a Treatment Planning System and the MCNPX Code

Introduction: This study investigates to what extent the computed dose received by lung tissue in a commercially available treatment planning system (TPS) for 192Ir high-dose-rate breast brachytherapy is accurate in view of tissue inhomogeneities and presence of ribs. Materials and Methods: A CT scan of the breast was used to construct a patient-equivalent phantom in the clinical treatment planning system. An implant involving 13 plastic catheters and 383 programmed source dwell positions were simulated using the MCNPX code. Results: The results were compared with the corresponding commercial TPS in the form of isodoses and cumulative dose–volume histogram in breast, lung and ribs. The comparison of Monte Carlo results and TPS calculation showed that the isodoses greater than 62% in the breast that were located rather close to the implant or away from the breast curvature surface and lung boundary were in good agreement. TPS calculations, however, overestimated dose in the lung for lower isodose contours and points that were lying near the breast-air boundary and relatively away from the implant. Discussion and Conclusions: Taking into account the ribs and entering the actual data for breast, rib and lung, revealed an average overestimation of dose in lung in the TPS calculation

A Novel Dual Energy CT-Based Attenuation Correction Method in PET/CT Systems: A Phantom Study A Novel DECT Attenuation Correction Method in PET/CT Teimourian et al

ABSTRACT In present PET/CT scanners, PET attenuation correction is performed by relying on the information given by CT scan. In the CT-based attenuation correction methods, dual-energy technique (DECT) is the most accurate approach, which has been limited due to the increasing patient dose. In this feasibility study, we have introduced a new method that can implement dual-energy technique with only a single energy CT scan. The implementation was done by CT scans of RANDO phantom at tube voltages of 80 kV P and 140 kV P . The acquired data was used to obtain conversion curves (which scale CT numbers at different kV P to each other), in three regions including lung tissue (HU<-100), soft tissue (-100<HU<200) and bone tissue (HU>200) for the combination of 80 kV P /140 kV P . Therefore, with having the CT image in one energy, we generate the CT image at the second energy (from now we call it virtual dual-energy technique) using these kV P conversion curves. The attenuation map at 511 keV was generated using bilinear (the most commonly used method in commercially available PET/CT scanners), real dual-energy and virtual dual-energy technique in a polyethylene phantom. In the phantom study, the created attenuation map using mentioned methods are compared to the theoretical values calculated using XCOM cross section library. The results in the phantom data show 10.1 %, 4.2 % and 4.3 % errors for bilinear, dual-energy and virtual dual-energy techniques respectively. Further evaluation using a larger patient data is underway to evaluate the potential of the technique in a clinical setting