ADC Model Research Articles

Comparison in prostate cancer diagnosis with PSA 4–10 ng/mL: radiomics-based model VS. PI-RADS v2.1

BackgroundTo evaluate accuracy of MRI-based radiomics in diagnosing prostate cancer (PCa) in patients with PSA levels between 4 and 10 ng/mL and compare it with the latest Prostate Imaging Reporting and Data System (PI-RADS v2.1) score.Methods221 patients with prostate lesions and PSA levels in 4–10 ng/mL, including 154 and 67 cases in the training and validation groups. Pathological confirmation of all patients was accomplished by the use of MRI-TRUS fusion targeted biopsy or systematic transrectal ultrasound (TRUS) guided biopsy. 851 radiomic features were extracted from each lesion of ADC and T2WI images. The least absolute shrinkage and selection operator (LASSO) regression algorithm and logistic regression were employed to select features and build the ADC and T2WI model. The combined model was obtained based on the ADC and T2WI features. The clinical benefit and diagnostic accuracy of the three radiomics models and PI-RADS v2.1 score were evaluated.Results10 radiomic features were ultimately selected from the ADC images, 13 from the T2WI images and 7 from the combined models. The ADC, T2WI and combined models achieved satisfactory diagnostic accuracy in the training [AUC:0.945 (ADC), 0.939 (T2WI), 0.979 (combined)] and validation groups [AUC: 0.942 (ADC), 0.943 (T2WI), 0.959 (combined)], which was significantly higher than those in PI-RADS v2.1 model (0.825 for training cohort and 0.853 for validation cohort). Compared with the PI-RADS v2.1 score, the three radiomics models generated superior PCa diagnostic performance in both the training (p = 0.002, p = 0.005, p < 0.001) and validation groups (p = 0.045, p = 0.035, p = 0.015).ConclusionRadiomics based on ADC and T2WI images can better identify PCa in patients with PSA 4–10 ng/mL, and MRI-based radiomics significantly outperforms the PI-RADS v2.1 score.Clinical trial numberNot applicable.

Quantitative Multi-Parametric MRI of the Prostate Reveals Racial Differences

Purpose: This study investigates whether quantitative MRI and histology of the prostate reveal differences between races, specifically African Americans (AAs) and Caucasian Americans (CAs), that can affect diagnosis. Materials and Methods: Patients (98 CAs, 47 AAs) with known or suspected prostate cancer (PCa) underwent 3T MRI (T2W, DWI, and DCE-MRI) prior to biopsy or prostatectomy. Quantitative mpMRI metrics: ADC, T2, and DCE empirical mathematical model parameters were calculated. Results: AAs had a greater percentage of higher Gleason-grade lesions compared to CAs. There were no significant differences in the quantitative ADC and T2 values between AAs and CAs. The cancer signal enhancement rate (α) on DCE-MRI was significantly higher for AAs compared to CAs (AAs: 13.3 ± 9.3 vs. CAs: 6.1 ± 4.7 s−1, p &lt; 0.001). The DCE signal washout rate (β) was significantly lower in benign tissue of AAs (AAs: 0.01 ± 0.09 s−1 vs. CAs: 0.07 ± 0.07 s−1, p &lt; 0.001) and significantly elevated in cancer tissue in AAs (AAs: 0.12 ± 0.07 s−1 vs. CAs: 0.07 ± 0.08 s−1, p = 0.02). DCE significantly improves the differentiation of PCa from benign in AAs (α: 52%, β: 62% more effective in AAs compared to CAs). Histologic analysis showed cancers have a greater proportion (p = 0.04) of epithelium (50.9 ± 12.3 vs. 44.7 ± 12.8%) and lower lumen (10.5 ± 6.9 vs. 16.2 ± 6.8%) in CAs compared to AAs. Conclusions: This study shows that AAs have different quantitative DCE-MRI values for benign prostate and prostate cancer and different histologic makeup in PCa compared to CAs. Quantitative DCE-MRI can significantly improve the performance of MRI for PCa diagnosis in African Americans but is much less effective for Caucasian Americans.

Electrothermal Averaged Model of a Half-Bridge DC–DC Converter Containing a Power Module

This article proposes an electrothermal averaged model of a half-bridge DC–DC converter containing a power module. This kind of model enables the computation of characteristics of DC–DC converters using DC analysis. The form of the elaborated model is presented. Both the electrical and thermal properties of the analyzed DC–DC converter are included in this model. This is the first averaged electrothermal model of a DC–DC converter which makes it possible to compute the junction temperature of all the semiconductor devices and magnetic components. The accuracy of the model was experimentally verified in a wide range of switching frequencies and output currents. Particularly, the influence of mutual thermal couplings between the transistors contained in the considered module on the characteristics of the converter and the junction temperature of the transistors is analyzed.

Infinite-time robust optimal output tracking of continuous-time linear systems using undiscounted reinforcement learning

This research paper focuses on addressing the challenge of infinite-time linear quadratic tracking control (LQT) for linear systems with parametric uncertainty. Traditional solutions to the LQT problem often involve using a discount factor to prevent the cost function from growing unbounded over time. However, this approach can introduce instability in the closed-loop system. To overcome this issue, this paper proposes an alternative approach using an undiscounted cost function that ensures the asymptotic stability of the uncertain closed-loop system. To design a control scheme without requiring precise knowledge of the system dynamics, reinforcement learning (RL) algorithms are employed. However, for systems with uncertain parameters that may lead to instability, the convergence of RL algorithms to a stabilising solution is not guaranteed. To address this limitation, a robust optimal control structure is developed using on-policy and off-policy reinforcement learning algorithms, resulting in a model-free controller. The effectiveness of the proposed robust optimal controller is validated through comparative simulations on an uncertain model of a DC–DC buck converter connected to a constant power load. These simulations demonstrate the advantages and benefits of the robust optimal controller in handling parametric uncertainty and ensuring stability in the control system.

Data Link Effectiveness Evaluation Method Based on Improved ADC Model

Aiming at the problem of innumerable data link effectiveness evaluation bases, the battlefield environment adaptability is introduced into the ADC model to improve the accuracy of the ADC evaluation method based on the analysis of the ADC model. According to the mission and using mode of data link equipment, the evaluation index system and calculation method of data link equipment effectiveness are proposed, and the quantitative evaluation of data link system effectiveness is realized. The validity and scientificity of the model are verified by the example analysis, which provides an effective method for the effectiveness evaluation of data link equipment.

Pipelined Memristive neural network analog-to-digital converter

This paper designs a pipelined memristive neural network ADCs. Cascade the sub stages of memristive neural network ADC with pipeline structure to improve the conversion accuracy of memristive neural network ADC. First, the signal flow of the pipeline architecture is optimized, and the compatibility between the 4 bit memristive neural network ADC and the pipeline architecture is solved. Secondly, the application of random disturbance circuits in pipeline architecture was analyzed. Combining the calibration circuit with the pipeline DAC to reduce the power consumption of the calibration circuit. Finally, the circuit model of memristive neural network ADC is built and compared with the existing memristive neural network ADC. The results indicate that the pipeline structure ADC designed in this chapter has the advantage of adaptive calibration in terms of calibration function.

Multiparametric MRI-based radiomics model for predicting human papillomavirus status in oropharyngeal squamous cell carcinoma: optimization using oversampling and machine learning techniques.

To develop and validate a multiparametric MRI-based radiomics model with optimal oversampling and machine learning techniques for predicting human papillomavirus (HPV) status in oropharyngeal squamous cell carcinoma (OPSCC). This retrospective, multicenter study included consecutive patients with newly diagnosed and pathologically confirmed OPSCC between January 2017 and December 2020 (110 patients in the training set, 44 patients in the external validation set). A total of 293 radiomics features were extracted from three sequences (T2-weighted images [T2WI], contrast-enhanced T1-weighted images [CE-T1WI], and ADC). Combinations of three feature selection, five oversampling, and 12 machine learning techniques were evaluated to optimize its diagnostic performance. The area under the receiver operating characteristic curve (AUC) of the top five models was validated in the external validation set. A total of 154 patients (59.2 ± 9.1years; 132 men [85.7%]) were included, and oversampling was employed to account for data imbalance between HPV-positive and HPV-negative OPSCC (86.4% [133/154] vs. 13.6% [21/154]). For the ADC radiomics model, the combination of random oversampling and ridge showed the highest diagnostic performance in the external validation set (AUC, 0.791; 95% CI, 0.775-0.808). The ADC radiomics model showed a higher trend in diagnostic performance compared to the radiomics model using CE-T1WI (AUC, 0.604; 95% CI, 0.590-0.618), T2WI (AUC, 0.695; 95% CI, 0.673-0.717), and a combination of both (AUC, 0.642; 95% CI, 0.626-0.657). The ADC radiomics model using random oversampling and ridge showed the highest diagnostic performance in predicting the HPV status of OPSCC in the external validation set. Among multiple sequences, the ADC radiomics model has a potential for generalizability and applicability in clinical practice. Exploring multiple oversampling and machine learning techniques was a valuable strategy for optimizing radiomics model performance. • Previous radiomics studies using multiparametric MRI were conducted at single centers without external validation and had unresolved data imbalances. • Among the ADC, CE-T1WI, and T2WI radiomics models and the ADC histogram models, the ADC radiomics model was the best-performing model for predicting human papillomavirus status in oropharyngeal squamous cell carcinoma. • The ADC radiomics model with the combination of random oversampling and ridge showed the highest diagnostic performance.

Learning ADC maps from accelerated radial k-space diffusion-weighted MRI in mice using a deep CNN-transformer model.

To accelerate radially sampled diffusion weighted spin-echo (Rad-DW-SE) acquisition method for generating high quality ADC maps. A deep learning method was developed to generate accurate ADC maps from accelerated DWI data acquired with the Rad-DW-SE method. The deep learning method integrates convolutional neural networks (CNNs) with vision transformers to generate high quality ADC maps from accelerated DWI data, regularized by a monoexponential ADC model fitting term. A model was trained on DWI data of 147 mice and evaluated on DWI data of 36 mice, with acceleration factors of 4× and 8× compared to the original acquisition parameters. Ablation studies and experimental results have demonstrated that the proposed deep learning model generates higher quality ADC maps from accelerated DWI data than alternative deep learning methods under comparison when their performance is quantified in whole images as well as in regions of interest, including tumors, kidneys, and muscles. The deep learning method with integrated CNNs and transformers provides an effective means to accurately compute ADC maps from accelerated DWI data acquired with the Rad-DW-SE method.

Multi-view radiomics and deep learning modeling for prostate cancer detection based on multi-parametric MRI.

This study aims to develop an imaging model based on multi-parametric MR images for distinguishing between prostate cancer (PCa) and prostate hyperplasia. A total of 236 subjects were enrolled and divided into training and test sets for model construction. Firstly, a multi-view radiomics modeling strategy was designed in which different combinations of radiomics feature categories (original, LoG, and wavelet) were compared to obtain the optimal input feature sets. Minimum-redundancy maximum-relevance (mRMR) selection and least absolute shrinkage selection operator (LASSO) were used for feature reduction, and the next logistic regression method was used for model construction. Then, a Swin Transformer architecture was designed and trained using transfer learning techniques to construct the deep learning models (DL). Finally, the constructed multi-view radiomics and DL models were combined and compared for model selection and nomogram construction. The prediction accuracy, consistency, and clinical benefit were comprehensively evaluated in the model comparison. The optimal input feature set was found when LoG and wavelet features were combined, while 22 and 17 radiomic features in this set were selected to construct the ADC and T2 multi-view radiomic models, respectively. ADC and T2 DL models were built by transferring learning from a large number of natural images to a relatively small sample of prostate images. All individual and combined models showed good predictive accuracy, consistency, and clinical benefit. Compared with using only an ADC-based model, adding a T2-based model to the combined model would reduce the model's predictive performance. The ADCCombinedScore model showed the best predictive performance among all and was transformed into a nomogram for better use in clinics. The constructed models in our study can be used as a predictor in differentiating PCa and BPH, thus helping clinicians make better clinical treatment decisions and reducing unnecessary prostate biopsies.

A Novel Two-Stage Timing Mismatch Calibration Technique for Time-Interleaved ADCs

This brief proposes a timing mismatch calibration for time-interleaved analog-to-digital converters (TI ADCs) with the novel parallel correlation derivative (PCD) technique and two-stage analog–digital hybrid compensation. The PCD technique could solve the frequency-relevant problem caused by low correlation derivative in the precise skew calculation. Besides, the compensation scheme with an analog coarse correction and then the all-digital fine correction is used to cover a larger normalized skew range and bandwidth with maintained calibration performance. Compared to previous works on timing mismatch calibration, this work has improved accuracy with a larger effective bandwidth and a larger skew calibration range. The technique is applied in a four-channel 14-bit 3 GS/s ADC model to verify its effectiveness. Simulation results show that it increases the SNDR and SFDR from 31.02 and 32.87 to 54.40 and 93.49 dB at <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\textit{f}_{\text{in}}$</tex-math> </inline-formula> <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$=$</tex-math> </inline-formula> 0.99 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\textit{f}_{\textit{s}}$</tex-math> </inline-formula> and maintains good performance in the first three Nyquist bands.

Cooperative behavior in the workplace: Empirical evidence from the agent-deed-consequences model of moral judgment.

Moral judgment is of critical importance in the work context because of its implicit or explicit omnipresence in a wide range of work-place practices. The moral aspects of actual behaviors, intentions, and consequences represent areas of deep preoccupation, as exemplified in current corporate social responsibility programs, yet there remain ongoing debates on the best understanding of how such aspects of morality (behaviors, intentions, and consequences) interact. The ADC Model of moral judgment integrates the theoretical insights of three major moral theories (virtue ethics, deontology, and consequentialism) into a single model, which explains how moral judgment occurs in parallel evaluation processes of three different components: the character of a person (Agent-component); their actions (Deed-component); and the consequences brought about in the situation (Consequences-component). The model offers the possibility of overcoming difficulties encountered by single or dual-component theories. We designed a 2 × 2 × 2-between-subjects design vignette experiment with a Germany-wide sample of employed respondents (N = 1,349) to test this model. Results showed that the Deed-component affects willingness to cooperate in the work context, which is mediated via moral judgments. These effects also varied depending on the levels of the Agent- and Consequences-component. Thereby, the results exemplify the usefulness of the ADC Model in the work context by showing how the distinct components of morality affect moral judgment.

Accurate Prediction of Vertical Ionization Potentials and Electron Affinities from Spin-Component Scaled CC2 and ADC(2) Models.

The CC2 and ADC(2) wave function models and their spin-component scaled modifications are adopted for predicting vertical ionization potentials (VIPs) and electron affinities (VEAs). The ionic solutions are obtained as electronic excitations in the continuum orbital formalism, making possible the use of existing, widespread quantum chemistry codes with minimal modifications, in full consistency with the treatment of charge transfer excitations. The performance of different variants is evaluated via benchmark calculations on various sets from previous works, containing small- and medium-sized systems, including the nucleobases. It is shown that with the spin-scaled approximate methods, in particular the scaled opposite-spin variant of the ADC(2) method, the accuracy of EOM-CCSD is achievable at a fraction of the computational cost, also outperforming many common electron propagator approaches.

Multiparametric MR radiomics in brain glioma: models comparation to predict biomarker status

BackgroundGenotype status of glioma have important significance to clinical treatment and prognosis. At present, there are few studies on the prediction of multiple genotype status in glioma by method of multi-sequence radiomics. The purpose of the study is to compare the performance of clinical features (age, sex, WHO grade, MRI morphological features etc.), radiomics features from multi MR sequence (T2WI, T1WI, DWI, ADC, CE-MRI (contrast enhancement)), and a combined multiple features model in predicting biomarker status (IDH, MGMT, TERT, 1p/19q of glioma.MethodsIn this retrospective analysis, 81 glioma patients confirmed by histology were enrolled in this study. Five MRI sequences were used for radiomic feature extraction. Finally, 107 features were extracted from each sequence on Pyradiomics software, separately. These included 18 first-order metrics, such as the mean, standard deviation, skewness, and kurtosis etc., 14 shape features and second-order metrics including 24 grey level run length matrix (GLCM), 16 grey level run length matrix (GLRLM), 16 grey level size zone matrix (GLSZM), 5 neighboring gray tone difference matrix (NGTDM), and 14 grey level dependence matrix (GLDM). Then, Univariate analysis and LASSO (Least absolute shrinkage and selection operator regression model were used to data dimension reduction, feature selection, and radiomics signature building. Significant features (p < 0.05 by multivariate logistic regression were retained to establish clinical model, T1WI model, T2WI model, T1 + C (T1WI contrast enhancement model, DWI model and ADC model, multi sequence model. Clinical features were combined with multi sequence model to establish a combined model. The predictive performance was validated by receiver operating characteristic curve (ROC analysis and decision curve analysis (DCA).ResultsThe combined model showed the better performance in some groups of genotype status among some models (IDH AUC = 0.93, MGMT AUC = 0.88, TERT AUC = 0.76). Multi sequence model performed better than single sequence model in IDH, MGMT, TERT. There was no significant difference among the models in predicting 1p/19q status. Decision curve analysis showed combined model has higher clinical benefit than multi sequence model.ConclusionMulti sequence model is an effective method to identify the genotype status of cerebral glioma. Combined with clinical models can better distinguish genotype status of glioma.Key PointsThe combined model showed the higher performance compare with other models in predicting genotype status of IDH, MGMT, TERT.Multi sequence model showed a better predictive model than that of a single sequence model.Compared with other models, the combined model and multi sequence model show no advantage in prediction of 1p/19q status.

Apparent diffusion coefficient and intravoxel incoherent motion-diffusion kurtosis model parameters in invasive breast cancer: Correlation with the histological parameters of whole-slide imaging

ObjectivesTo evaluate the correlation of ADC and IVIM-diffusion kurtosis (DK) model parameters with quantitative histological parameters of whole-slide imaging (WSI). MethodsThis retrospective study (September 2015–July 2016) included 37 consecutive patients (all females; median age: 64 years, range, 41–82 years), each with a single invasive breast ductal carcinoma lesion with a mass appearance on preoperative MRI. DWI with b-values of 0, 50, 100, 300, 550, 850, and 1000 s/mm2 was performed. ADC maps were generated with b-values of 50 and 850 s/mm2. The IVIM-DK model was analysed using the following formula:Sb=S0fe−bD∗+1−fe−bD+1/6kbD2where S is the signal intensity, b is the b-value, f is the perfusion fraction, D* is the pseudo-diffusion coefficient of the vascular component, D is the diffusion coefficient of the non-vascular component, and k is the diffusion kurtosis. Whole tumour segmentation was performed to obtain the mean ADC, f, D*, D, and k. Quantitative histological parameters were obtained using cytokeratin immunostaining and WSI. The correlation of ADC and IVIM-DK model parameters with quantitative histological parameters was examined by Pearson's correlation. ResultsThe ADC was significantly correlated with the area ratio of interstitium (r = 0.53, p = 0.00082) and entropy (r = −0.58, p = 0.00019). k was significantly correlated with the area ratio of cancer cell nuclei (r = 0.53, p = 0.00079). ConclusionsSince the ADC reflected the area ratio of interstitium and entropy, and diffusion kurtosis reflected the area ratio of cancer cell nuclei, these parameters may be effective in distinguishing between benign and malignant breast tumours and in grading breast cancer.

Discriminating rectal cancer grades using restriction spectrum imaging.

Restriction spectrum imaging (RSI) is a novel diffusion MRI model that separates water diffusion into several microscopic compartments. The restricted compartment correlating to the tumor cellularity is expected to be a potential indicator of rectal cancer aggressiveness. Our aim was to assess the ability of RSI model for rectal tumor grading. Fifty-eight patients with different rectal cancer grading confirmed by biopsy were involved in this study. DWI acquisitions were performed using single-shot echo-planar imaging (SS-EPI) with multi-b-values at 3T. We applied a three-compartment RSI model, along with ADC model and diffusion kurtosis imaging (DKI) model, to DWI images of 58 patients. ROC and AUC were used to compare the performance of the three models in differentiating the low grade (G1 + G2) and high grade (G3). Mean ± standard deviation, ANOVA, ROC analysis, and correlation analysis were used in this study. The volume fraction of restricted compartment C1 from RSI was significantly correlated with grades (r = 0.403, P = 0.002). It showed significant difference between G1 and G3 (P = 0.008) and between G2 and G3 (P = 0.01). As for the low-grade and high-grade discrimination, significant difference was found in C1 (P < 0.001). The AUC of C1 for differentiation between low-grade and high-grade groups was 0.753 with a sensitivity of 72.0% and a specificity of 70.0%. The three-compartment RSI model was able to discriminate the rectal cancer of low and high grades. The results outperform the traditional ADC model and DKI model in rectal cancer grading.

The Classification Power of Classical and Intra-voxel Incoherent Motion (IVIM) Fitting Models of Diffusion-weighted Magnetic Resonance Images: An Experimental Study.

This study aims at investigating the classification power of different b-values of the diffusion-weighted magnetic resonance images (DWI) as indicator of prostate cancer. This paper investigates several techniques for analyzing data from DWI acquired at a range of b-values for the purpose of detecting prostate cancer. In the first phase of experiments, we analyze the available data by producing two main parametric maps using two common models, namely: the intra-voxel incoherent motion (IVIM) model and the mono-exponential ADC model. Accordingly, we evaluated the benign/malignant tissue classification potential of several parametric maps produced using different combinations of b-values and fitting models. In the second phase, we utilized the maps that performed best in the first phase of experiments to design a machine learning-based computer-assisted diagnosis system for the detection of early stage prostate cancer. The system performance was cross-validated using data from 20 patients. On a fivefold cross-validation scheme, a maximum accuracy and an area under the receiver operating characteristic (AUC) of 90% and 0.978, respectively, was achieved by a system that uses ADC maps fitted using the mono-exponential model at 11 different b-values. The results suggest that the proposed machine learning-based diagnosis system is potentially powerful in differentiating between malignant and benign prostate tissues when combined with carefully generated ADC maps.

Wideband SiN pulse interleaver for optically-enabled analog-to-digital conversion: a device-to-system analysis with cyclic equalization.

We present the design and experimental characterization of a silicon nitride pulse interleaver based on coupled resonator optical waveguide filters. In order to achieve a targeted free spectral range of 1.44 THz, which is large given the reduced optical confinement of the silicon nitride platform, individual ring resonators are designed with tapered waveguides. Its application to time-interleaved photonically-assisted ADCs is analyzed by combining experimental characterization of the photonic integrated circuit with a comprehensive model of the entire ADC. The impact of fundamental signal distortion and noise sources affecting the converter is investigated and suitable equalization techniques at the digital signal processing level are evaluated. The novel application of a simple but powerful equalization filter in the DSP domain allows for a significant improvement of the digitized signal SNR. An ENOB of 5 over a 75 GHz bandwidth (150 GS/s) and an ENOB of 4.3 over a 100 GHz bandwidth (200 GS/s) are expected to be achievable with compact and off-the-shelf single-section semiconductor mode locked lasers, that can be further improved with lower noise light sources.

ADC-GERT network parameter estimation model for mission effectiveness of joint operation system

Effectiveness evaluation of the joint operation system is an important basis for the demonstration and development of weapon equipment. With the consideration that existing models of system effectiveness evaluation seldom describe the structural relationship among equipment clearly as well as reflect the dynamic, the analog-to-digital converter-graphical evaluation and review technique (ADC-GERT) network parameter estimation model is proposed based on the ADC model and the joint operation system structure. Firstly, analysis of the joint operation system structure and operation process is conducted to build the GERT network, where equipment subsystems are nodes and activities are directed arches. Then the mission effectiveness of equipment subsystems is calculated by the ADC model. The probability transfer parameters are modified by the mission effectiveness of equipment subsystems based on the Bayesian theorem, with the ADC-GERT network parameter estimation model constructed. Finally, a case study is used to validate the efficiency and dynamic of the ADC-GERT network parameter estimation model.

First-principles interpretation of electron transport through single-molecule junctions using molecular dynamics of electron attached states

The electron transport through the single-molecule junction of benzene-1,4-diamine (BDA) is modelled using ab initio quantum-classical molecular dynamics of electron attached states. Observations on the nature of the process are made by time-resolved analysis of energy differences, non-adiabatic transition probabilities and the spatial distribution of the excess electron. The role of molecular vibrations that facilitate the transport by being responsible for the periodic behaviour of these quantities is shown using normal mode analysis. The results support a mechanism involving the electron's direct hopping between the electrodes, without its presence on the molecule, with the prime importance of the bending vibrations that periodically alter the molecule–electrode interactions. No relevant differences are found between results provided by the ADC(2) and SOS-ADC(2) excited state models. Our approach provides an alternative insight into the role of nuclear motions in the electron transport process, one which is more expressive from the chemical perspective.

Radiomics Analysis of Multi-Sequence MR Images For Predicting Microsatellite Instability Status Preoperatively in Rectal Cancer.

BackgroundImmunotherapy, adjuvant chemotherapy, and prognosis of colorectal cancer are associated with MSI. Biopsy pathology cannot fully reflect the MSI status and heterogeneity of rectal cancer.PurposeTo develop a radiomic-based model to preoperatively predict MSI status in rectal cancer on MRI.AssessmentThe patients were divided into two cohorts (training and testing) at a 7:3 ratio. Radiomics features, including intensity, texture, and shape, were extracted from the segmented volumes of interest based on T2-weighted and ADC imaging.Statistical TestsIndependent sample t test, Mann-Whitney test, the chi-squared test, Receiver operating characteristic curves, calibration curves, decision curve analysis and multi-variate logistic regression analysisResultsThe radiomics models were significantly associated with MSI status. The T2-based model showed an area under the curve of 0.870 with 95% CI: 0.794–0.945 (accuracy, 0.845; specificity, 0.714; sensitivity, 0.976) in training set and 0.895 with 95% CI, 0.777–1.000 (accuracy, 0.778; specificity, 0.887; sensitivity, 0.772) in testing set. The ADC-based model had an AUC of 0.790 with 95% CI: 0.794–0.945 (accuracy, 0.774; specificity, 0.714; sensitivity, 0.976) in training set and 0.796 with 95% CI, 0.777–1.000 (accuracy, 0.778; specificity, 0.889; sensitivity, 0.772) in testing set. The combined model integrating T2 and ADC features showed an AUC of 0.908 with 95% CI: 0.845–0.971 (accuracy, 0.857; specificity, 0.762; sensitivity, 0.952) in training set and 0.926 with 95% CI: 0.813-1.000 (accuracy, 0.852; specificity, 1.000; sensitivity, 0.778) in testing set. Calibration curve showed that the combined score had a good calibration degree, and the decision curve demonstrated that the combined score was of benefit for clinical use.Data ConclusionRadiomics analysis of T2W and ADC images showed significant relevance in the prediction of microsatellite status, and the accuracy of combined model of ADC and T2W features was better than either alone.