Radiomics Analysis Based on CT’s greater-omental caking for Predicting Pathological Grading of Pseudomyxoma Peritonei

Abstract Purpose: The objective of this study was to predict the preoperative pathological grading and survival period of Pseudomyxoma peritonei (PMP) by establishing models, including a radiomics model with greater mental caking as the imaging observation index, a clinical model including clinical indexes, and a combination model of these two.Methods: A total of 88 PMP patients were selected. Clinical data of patients, including age, sex, preoperative serum tumor markers [CEA, CA125, and CA199], survival time, and preoperative computed tomography (CT) images were analyzed. Three models (clinical model, radiomics model and joint model) were used to predict PMP pathological grading. The models’ diagnostic efficiency was compared and analyzed by building the receiver operating characteristic (ROC) curve. Simultaneously, the impact of PMP’s different pathological grades was evaluated.Results: The results showed that the radiomics model based on the CT’s greater omental caking, an area under the ROC curve ([AUC] = 0.878), and the combined model (AUC = 0.899) had diagnostic power n for determining PMP pathological grade.Conclusion: The imaging radiomics model based on CT greater omental caking can be used to predict PMP pathological grade, which is important in the treatment selection method and prognosis assessment.

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Predicting Amyloid Pathology in Mild Cognitive Impairment Using Radiomics Analysis of Magnetic Resonance Imaging

10.21203/rs.3.rs-16481/v1 ◽

2020 ◽

Author(s):

Yae Won Park ◽

Dongmin Choi ◽

Mina Park ◽

Sung Jun Ahn ◽

Sung Soo ahn ◽

...

Keyword(s):

Magnetic Resonance Imaging ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Magnetic Resonance ◽

Resonance Imaging ◽

Combined Model ◽

Test Set ◽

Clinical Model ◽

Model Based ◽

Clinical Profiles

Abstract Background: Noninvasive identification of amyloid β (Aβ) is important in mild cognitive impairment (MCI) patients for better clinical management. This study aimed to evaluate whether radiomics features in the hippocampus in MCI improve the prediction of cerebrospinal fluid (CSF) Aβ 42 status when integrated with clinical and genetic profiles.Methods: A total of 407 MCI subjects from the Alzheimer’s Disease Neuroimaging Initiative were allocated to the training (n = 324) and test (n = 83) sets. Radiomics features (n = 214) from the bilateral hippocampi were extracted from T1-weighted images of magnetic resonance imaging (MRI). A previously defined cutoff (< 192 pg/mL) was applied for CSF Aβ 42 status. After feature selection, random forest with subsampling methods were trained to predict the CSF Aβ 42 with three models: 1) a radiomics model; 2) a clinical model based on clinical and genetic profiles including demographics, APOE ε4 genotype, and neuropsychological tests; and 3) a combined model based on radiomics and clinical profiles. The prediction performance of the classifier was validated in the test set using the area under the receiver operating characteristic curve (AUC). Results: The radiomics model identified 33 radiomics features to predict CSF Aβ 42 , which showed an AUC of 0.674 in the best performing radiomics model in the test set. The clinical model identified 6 clinical features to predict CSF Aβ 42 , which showed an AUC of 0.758 in the best performing clinical model in the test set. The combined model based on radiomics and clinical profiles identified a total of 37 features (32 from radiomics and 5 from clinical features), showing an AUC of 0.823 in the best performing combined model test set, which showed the highest performance among the three models. Conclusions: Radiomics model from MRI can help predict CSF Aβ 42 status in MCI patients and potentially triage the patients for the invasive and costly Aβ test.

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Predicting Amyloid Pathology in Mild Cognitive Impairment Using Radiomics Analysis of Magnetic Resonance Imaging

Journal of Alzheimer s Disease ◽

10.3233/jad-200734 ◽

2020 ◽

pp. 1-9

Author(s):

Yae Won Park ◽

Dongmin Choi ◽

Mina Park ◽

Sung Jun Ahn ◽

Sung Soo Ahn ◽

...

Keyword(s):

Magnetic Resonance Imaging ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Magnetic Resonance ◽

Resonance Imaging ◽

Combined Model ◽

Test Set ◽

Clinical Model ◽

Model Based ◽

Clinical Profiles

Background: Noninvasive identification of amyloid-β (Aβ) is important for better clinical management of mild cognitive impairment (MCI) patients. Objective: To investigate whether radiomics features in the hippocampus in MCI improve the prediction of cerebrospinal fluid (CSF) Aβ 42 status when integrated with clinical profiles. Methods: A total of 407 MCI subjects from the Alzheimer’s Disease Neuroimaging Initiative were allocated to training (n = 324) and test (n = 83) sets. Radiomics features (n = 214) from the bilateral hippocampi were extracted from magnetic resonance imaging (MRI). A cut-off of <192 pg/mL was applied to define CSF Aβ 42 status. After feature selection, random forest with subsampling methods were utilized to develop three models with which to predict CSF Aβ 42: 1) a radiomics model; 2) a clinical model based on clinical profiles; and 3) a combined model based on radiomics and clinical profiles. The prediction performances thereof were validated in the test set. A prediction model using hippocampus volume was also developed and validated. Results: The best-performing radiomics model showed an area under the curve (AUC) of 0.674 in the test set. The best-performing clinical model showed an AUC of 0.758 in the test set. The best-performing combined model showed an AUC of 0.823 in the test set. The hippocampal volume model showed a lower performance, with an AUC of 0.543 in the test set. Conclusion: Radiomics models from MRI can help predict CSF Aβ 42 status in MCI patients and potentially triage the patients for invasive and costly Aβ tests.

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Integration of Evidence into a Detailed Clinical Model-based Electronic Nursing Record System

Healthcare Informatics Research ◽

10.4258/hir.2012.18.2.136 ◽

2012 ◽

Vol 18 (2) ◽

pp. 136 ◽

Cited By ~ 3

Author(s):

Hyeoun-Ae Park ◽

Yul Ha Min ◽

Eunjoo Jeon ◽

Eunja Chung

Keyword(s):

Record System ◽

Clinical Model ◽

Model Based ◽

Nursing Record

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MRI features of responsible contacts in vascular compressive trigeminal neuralgia and prediction modeling

Acta Radiologica ◽

10.1177/0284185120983971 ◽

2021 ◽

pp. 028418512098397

Author(s):

Yufei Zhao ◽

Jianhua Chen ◽

Rifeng Jiang ◽

Xue Xu ◽

Lin Lin ◽

...

Keyword(s):

Trigeminal Neuralgia ◽

Predictive Model ◽

Trigeminal Nerve ◽

Roc Curve ◽

Neurovascular Compression ◽

Diagnostic Efficiency ◽

Mri Features ◽

Vascular Origin ◽

Magnetic Resonance Imaging Mri ◽

Mri Characteristics

Background Multiple neurovascular contacts in patients with vascular compressive trigeminal neuralgia often challenge the diagnosis of responsible contacts. Purpose To analyze the magnetic resonance imaging (MRI) features of responsible contacts and establish a predictive model to accurately pinpoint the responsible contacts. Material and Methods Sixty-seven patients with unilateral trigeminal neuralgia were enrolled. A total of 153 definite contacts (45 responsible, 108 non-responsible) were analyzed for their MRI characteristics, including neurovascular compression (NVC) grading, distance from pons to contact (Dpons-contact), vascular origin of compressing vessels, diameter of vessel (Dvessel) and trigeminal nerve (Dtrigeminal nerve) at contact. The MRI characteristics of the responsible and non-responsible contacts were compared, and their diagnostic efficiencies were further evaluated using a receiver operating characteristic (ROC) curve. The significant MRI features were incorporated into the logistics regression analysis to build a predictive model for responsible contacts. Results Compared with non-responsible contacts, NVC grading and arterial compression ratio (84.44%) were significantly higher, Dpons-contact was significantly lower at responsible contacts ( P < 0.001, 0.002, and 0.033, respectively). NVC grading had a highest diagnostic area under the ROC curve (AUC) of 0.742, with a sensitivity of 64.44% and specificity of 75.00%. The logistic regression model showed a higher diagnostic efficiency, with an AUC of 0.808, sensitivity of 88.89%, and specificity of 62.04%. Conclusion Contact degree and position are important MRI features in identifying the responsible contacts of the trigeminal neuralgia. The logistic predictive model based on Dpons-contact, NVC grading, and vascular origin can qualitatively improve the prediction of responsible contacts for radiologists.

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A Model-Based Tool for Assessing the Impact of Land Use Change Scenarios on Flood Risk in Small-Scale River Systems—Part 1: Pre-Processing of Scenario Based Flood Characteristics for the Current State of Land Use

Hydrology ◽

10.3390/hydrology8030102 ◽

2021 ◽

Vol 8 (3) ◽

pp. 102

Author(s):

Frauke Kachholz ◽

Jens Tränckner

Keyword(s):

Land Use ◽

River Flow ◽

Land Use Changes ◽

Small Rivers ◽

Small Scale ◽

Model Parameters ◽

Ungauged Catchments ◽

Model Based ◽

Current State ◽

The Impact

Land use changes influence the water balance and often increase surface runoff. The resulting impacts on river flow, water level, and flood should be identified beforehand in the phase of spatial planning. In two consecutive papers, we develop a model-based decision support system for quantifying the hydrological and stream hydraulic impacts of land use changes. Part 1 presents the semi-automatic set-up of physically based hydrological and hydraulic models on the basis of geodata analysis for the current state. Appropriate hydrological model parameters for ungauged catchments are derived by a transfer from a calibrated model. In the regarded lowland river basins, parameters of surface and groundwater inflow turned out to be particularly important. While the calibration delivers very good to good model results for flow (Evol =2.4%, R = 0.84, NSE = 0.84), the model performance is good to satisfactory (Evol = −9.6%, R = 0.88, NSE = 0.59) in a different river system parametrized with the transfer procedure. After transferring the concept to a larger area with various small rivers, the current state is analyzed by running simulations based on statistical rainfall scenarios. Results include watercourse section-specific capacities and excess volumes in case of flooding. The developed approach can relatively quickly generate physically reliable and spatially high-resolution results. Part 2 builds on the data generated in part 1 and presents the subsequent approach to assess hydrologic/hydrodynamic impacts of potential land use changes.

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A Reference Framework to Combine Model-Based Design and AR to Improve Social Sustainability

Sustainability ◽

10.3390/su13042031 ◽

2021 ◽

Vol 13 (4) ◽

pp. 2031

Author(s):

Fabio Grandi ◽

Riccardo Karim Khamaisi ◽

Margherita Peruzzini ◽

Roberto Raffaeli ◽

Marcello Pellicciari

Keyword(s):

Positive Impact ◽

Cost Effective ◽

Digital Models ◽

Model Based ◽

Digital Contents ◽

Recent Developments ◽

Virtual Simulations ◽

Product And Process ◽

The Impact ◽

Time Information

Product and process digitalization is pervading numerous areas in the industry to improve quality and reduce costs. In particular, digital models enable virtual simulations to predict product and process performances, as well as to generate digital contents to improve the general workflow. Digital models can also contain additional contents (e.g., model-based design (MBD)) to provide online and on-time information about process operations and management, as well as to support operator activities. The recent developments in augmented reality (AR) offer new specific interfaces to promote the great diffusion of digital contents into industrial processes, thanks to flexible and robust applications, as well as cost-effective devices. However, the impact of AR applications on sustainability is still poorly explored in research. In this direction, this paper proposed an innovative approach to exploit MBD and introduce AR interfaces in the industry to support human intensive processes. Indeed, in those processes, the human contribution is still crucial to guaranteeing the expected product quality (e.g., quality inspection). The paper also analyzed how this new concept can benefit sustainability and define a set of metrics to assess the positive impact on sustainability, focusing on social aspects.

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Model-Based Control of Torque and Nitrogen Oxide Emissions in a Euro VI 3.0 L Diesel Engine through Rapid Prototyping

Energies ◽

10.3390/en14041107 ◽

2021 ◽

Vol 14 (4) ◽

pp. 1107

Author(s):

Stefano d’Ambrosio ◽

Roberto Finesso ◽

Gilles Hardy ◽

Andrea Manelli ◽

Alessandro Mancarella ◽

...

Keyword(s):

Diesel Engine ◽

Rapid Prototyping ◽

Nitrogen Oxide ◽

Thermal State ◽

Pollutant Emissions ◽

Computational Time ◽

Nox Emissions ◽

Model Based ◽

Brake Mean Effective Pressure ◽

The Impact

In the present paper, a model-based controller of engine torque and engine-out Nitrogen oxide (NOx) emissions, which was previously developed and tested by means of offline simulations, has been validated on a FPT F1C 3.0 L diesel engine by means of rapid prototyping. With reference to the previous version, a new NOx model has been implemented to improve robustness in terms of NOx prediction. The experimental tests have confirmed the basic functionality of the controller in transient conditions, over different load ramps at fixed engine speeds, over which the average RMSE (Root Mean Square Error) values for the control of NOx emissions were of the order of 55–90 ppm, while the average RMSE values for the control of brake mean effective pressure (BMEP) were of the order of 0.25–0.39 bar. However, the test results also highlighted the need for further improvements, especially concerning the effect of the engine thermal state on the NOx emissions in transient operation. Moreover, several aspects, such as the check of the computational time, the impact of the controller on other pollutant emissions, or on the long-term engine operations, will have to be evaluated in future studies in view of the controller implementation on the engine control unit.

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