Sums Of Variables Research Articles

Tighter sum unitary uncertainty relation

Abstract Unitary uncertainty relations provide a theoretical framework that enhances our understanding of the principles underlying quantum mechanics and its applications in quantum information science. In this study, we derive both the unitary uncertainty relation and the weighted unitary uncertainty relation based on the sum variance for arbitrary pairs of unitary operators. By applying the arithmetic geometric mean inequality, we obtain a lower bound that is tighter than the one provided by Bagchi and Pati. [Physical Review A 94,042104] for two unitary operators. To illustrate our results, we include examples of both the unitary uncertainty relation and the weighted uncertainty relation based on sum variance.

Equivalence of cost concentration and gradient vanishing for quantum circuits: an elementary proof in the Riemannian formulation

Abstract The optimization of quantum circuits can be hampered by a decay of average gradient amplitudes with increasing system size. When the decay is exponential, this is called the barren plateau problem. Considering explicit circuit parametrizations (in terms of rotation angles), it has been shown in Arrasmith et al (2022 Quantum Sci. Technol. 7 045015) that barren plateaus are equivalent to an exponential decay of the variance of cost-function differences. We show that the issue is particularly simple in the (parametrization-free) Riemannian formulation of such optimization problems and obtain a tighter bound for the cost-function variance. An elementary derivation shows that the single-gate variance of the cost function is strictly equal to half the variance of the Riemannian single-gate gradient, where we sample variable gates according to the uniform Haar measure. The total variances of the cost function and its gradient are then both bounded from above by the sum of single-gate variances and, conversely, bound single-gate variances from above. So, decays of gradients and cost-function variations go hand in hand, and barren plateau problems cannot be resolved by avoiding gradient-based in favor of gradient-free optimization methods.

Harnessing 3D seismic data for mapping natural CO2 distribution: Paving the way for future CCS/CCUS implementation in the Lower Talang Akar Formation, Jabung Block, South Sumatra Basin

Hydrocarbon reservoirs saturated with CO2 are naturally widespread in the Lower Talang Akar Formation (LTAF) and deposited in the Jabung Block, South Sumatra Basin, Indonesia. The different CO2 saturation contents stored in the reservoirs pose a challenge to planning future carbon capture and storage (CCS) and carbon capture, utilization, and storage (CCUS) in the area. This study integrates poststack 3D seismic data and existing wells in the western part of the Jabung Block. It utilizes seismic attribute methods such as sum of negative amplitude, relative impedance, variance, and ant tracking to predict the origin, saturation, and distribution of CO2-saturated hydrocarbon accumulations in the sandstone reservoirs of the LTAF. The results show that CO2 originated from the Betara Deep source rock in the study area's eastern side and migrated to the reservoirs through major northwest–southeast-trending fault zones. The saturation of CO2 in the reservoirs is mainly controlled by the distance from the source and the reservoir depths, faulting, and fracturing intensity. The fields closer to the Betara source rock tend to be filled with higher contents of CO2 saturation. The results provide valuable guidance in CO2 identification and mapping for future CCS and CCUS implementation in the study area.

MRI Radiomics Data Analysis for Differentiation between Malignant Mixed Müllerian Tumors and Endometrial Carcinoma.

The objective of this study was to compare the quantitative radiomics data between malignant mixed Müllerian tumors (MMMTs) and endometrial carcinoma (EC) and identify texture features associated with overall survival (OS). This study included 61 patients (36 with EC and 25 with MMMTs) and analyzed various radiomic features and gray-level co-occurrence matrix (GLCM) features. These variables and patient clinicopathologic characteristics were compared between EC and MMMTs using the Wilcoxon Rank sum and Fisher's exact test. The area under the curve of the receiving operating characteristics (AUC ROC) was calculated for univariate analysis in predicting EC status. Logistic regression with elastic net regularization was performed for texture feature selection. This study showed that skewness (p = 0.045) and tumor volume (p = 0.007) significantly differed between EC and MMMTs. The range of cluster shade, the angular variance of cluster shade, and the range of the sum of squares variance were significant predictors of EC status (p ≤ 0.05). The regularized Cox regression analysis identified the "256 Angular Variance of Energy" texture feature as significantly associated with OS independently of the EC/MMMT grouping (p = 0.004). The volume and texture features of the tumor region may help distinguish between EC and MMMTs and predict patient outcomes.

Machine learning prediction model for gray-level co-occurrence matrix features of synchronous liver metastasis in colorectal cancer.

Synchronous liver metastasis (SLM) is a significant contributor to morbidity in colorectal cancer (CRC). There are no effective predictive device integration algorithms to predict adverse SLM events during the diagnosis of CRC. To explore the risk factors for SLM in CRC and construct a visual prediction model based on gray-level co-occurrence matrix (GLCM) features collected from magnetic resonance imaging (MRI). Our study retrospectively enrolled 392 patients with CRC from Yichang Central People's Hospital from January 2015 to May 2023. Patients were randomly divided into a training and validation group (3:7). The clinical parameters and GLCM features extracted from MRI were included as candidate variables. The prediction model was constructed using a generalized linear regression model, random forest model (RFM), and artificial neural network model. Receiver operating characteristic curves and decision curves were used to evaluate the prediction model. Among the 392 patients, 48 had SLM (12.24%). We obtained fourteen GLCM imaging data for variable screening of SLM prediction models. Inverse difference, mean sum, sum entropy, sum variance, sum of squares, energy, and difference variance were listed as candidate variables, and the prediction efficiency (area under the curve) of the subsequent RFM in the training set and internal validation set was 0.917 [95% confidence interval (95%CI): 0.866-0.968] and 0.09 (95%CI: 0.858-0.960), respectively. A predictive model combining GLCM image features with machine learning can predict SLM in CRC. This model can assist clinicians in making timely and personalized clinical decisions.

Variance sum rule: proofs and solvable models

We derive, in more general conditions, a recently introduced variance sum rule (VSR) (Di Terlizzi et al 2024 Science 383 971) involving variances of displacement and force impulse for overdamped Langevin systems in a nonequilibrium steady state (NESS). This formula allows visualising the effect of nonequilibrium as a deviation of the sum of variances from normal diffusion 2Dt, with D the diffusion constant and t the time. From the VSR, we also derive formulas for the entropy production rate σ that, differently from previous results, involve second-order time derivatives of position correlation functions. This novel feature gives a criterion for discriminating strong nonequilibrium regimes without measuring forces. We then apply and discuss our results to three analytically solved models: a stochastic switching trap, a Brownian vortex, and a Brownian gyrator. Finally, we compare the advantages and limitations of known and novel formulas for σ in an overdamped NESS.

Partitioning among-animal variance of energy utilization in lactating Jersey cows

Animals vary in the way in which they utilize energy due to diet, genetics, and management. Energy consumed by the animal supports milk production, but considerable variation among-animals in energy utilization is thought to exist. The study objective was to estimate the among-animal variance in energy utilization in data collected from Jersey cows using indirect calorimetry. Individual animal-period data from 15 studies (n = 560) were used. The data set included 115 animals from 44 to 410 DIM producing 11.5 to 39.1 kg/d of milk. On average, the 63 treatments in the data set ranged 14.8 to 19.5% CP, 21.4 to 43.0% NDF, 16.2 to 33.3% starch, and 2.21 to 6.44% crude fat. Data were analyzed with the Glimmix procedure of SAS (9.4) with random effects of cow, treatment nested within period, square, and experiment. The percentage of among-animal, dietary treatment, and experimental variance was calculated as the variance associated with each fraction divided by the sum of variance from animal, dietary treatment, experiment, and residual which was considered the total variance. The percentage of among-animal variance was characterized as high or low when the value was greater than or less than the mean value of 29.2%. Among-animal variance explained approximately 29.3 - 42.5% of the total variance in DM intake (DMI), gross energy (GE), digestible energy (DE), metabolizable energy (ME), and net energy of lactation (NEL) in Mcal/d. When energetic components of feces, urine, and heat in Mcal/d were expressed per unit of DMI the among-animal variance decreased by 20.4, 4.82, and 9.55% units, respectively. However, among-animal variance explained 4.80, 8.78, and 5.02% units more of the total variation for methane energy, lactation energy, and tissue energy in Mcal/d when expressed per unit of DMI. Variance in energetic efficiencies of DE/GE, ME/GE, and ME/DE were explained to a lesser extent by among-animal variance (averaging 17.8 ± 1.95%). The among-animal contribution to total variance in milk energy was 28.8%. Milk energy was a large proportion of the energy efficiency calculation which included milk energy plus corrected tissue energy over net energy intake which likely contributed to the 22.2% of total among-animal variance in energy efficiency. Results indicate that among-animal variance explains a large proportion of the total variation in DMI. This contributes to the variance observed for energy fractions as well as energy components when expressed in Mcal/d. Variation in energetic loss associated with methane was primarily explained by differences among-animals and was increased when expressed per unit of DMI highlighting the role of inherent animal differences in these losses.

Radiogenomics Map-Based Molecular and Imaging Phenotypical Characterization in Localised Prostate Cancer Using Pre-Biopsy Biparametric MR Imaging.

To create a radiogenomics map and evaluate the correlation between molecular and imaging phenotypes in localized prostate cancer (PCa), using radical prostatectomy histopathology as a reference standard. Radiomic features were extracted from T2-weighted (T2WI) and Apparent Diffusion Coefficient (ADC) images of clinically localized PCa patients (n = 15) across different Gleason score-based risk categories. DNA extraction was performed on formalin-fixed, paraffin-embedded (FFPE) samples. Gene expression analysis of androgen receptor expression, apoptosis, and hypoxia was conducted using the Chromosome Analysis Suite (ChAS) application and OSCHIP files. The relationship between gene expression alterations and textural features was assessed using Pearson's correlation analysis. Receiver operating characteristic (ROC) analysis was utilized to evaluate the predictive accuracy of the model. A significant correlation was observed between radiomic texture features and copy number variation (CNV) of genes associated with apoptosis, hypoxia, and androgen receptor (p-value ≤ 0.05). The identified radiomic features, including Sum Entropy ADC, Inverse Difference ADC, Sum Variance T2WI, Entropy T2WI, Difference Variance T2WI, and Angular Secondary Moment T2WI, exhibited potential for predicting cancer grade and biological processes such as apoptosis and hypoxia. Incorporating radiomics and genomics into a prediction model significantly improved the prediction of prostate cancer grade (clinically significant prostate cancer), yielding an AUC of 0.95. Radiomic texture features significantly correlate with genotypes for apoptosis, hypoxia, and androgen receptor expression in localised prostate cancer. Integration of these into the prediction model improved prediction accuracy of clinically significant prostate cancer.

Uncertainty relation and the constrained quadratic programming

The uncertainty relation is a fundamental concept in quantum theory, plays a pivotal role in various quantum information processing tasks. In this study, we explore the additive uncertainty relation pertaining to two or more observables, in terms of their variance, by utilizing the generalized Gell-Mann representation in qudit systems. We find that the tight state-independent lower bound of the variance sum can be characterized as a quadratic programming problem with nonlinear constraints in optimization theory. As illustrative examples, we derive analytical solutions for these quadratic programming problems in lower-dimensional systems, which align with the state-independent lower bounds. Additionally, we introduce a numerical algorithm tailored for solving these quadratic programming instances, highlighting its efficiency and accuracy. The advantage of our approach lies in its potential ability to simultaneously achieve the optimal value of the quadratic programming problem with nonlinear constraints but also precisely identify the extremal state where this optimal value is attained. This enables us to establish a tight state-independent lower bound for the sum of variances, and further identify the extremal state at which this lower bound is realized.

Collaborative Optimization of Dynamic Lane Assignment and Signal Timing for Incident-Affected Intersections

Traffic accidents can lead to rapid changes in the traffic supply and demand at urban intersections, thus causing a severe traffic supply/demand imbalance during specific periods. Responding to accidents requires dynamic and accurate adjustments to optimize intersection resources collaboratively and enhance the real-time reliability and stability of traffic flows during such accidents. Currently, research on traffic incidents rarely considers real-time data-based collaborative optimization theories. Therefore, this study, supported by real-time incident detection technology and accident data, first considers the location and intensity of traffic incidents to update dynamically the changes in intersection traffic demand and supply. Subsequently, a dynamically collaborative optimization method is proposed based on lane assignment and signal timings to minimize the sum of variances of the degree of saturation of various approach lanes. Finally, various traffic demand scenarios are set, and the effectiveness of the proposed model is validated based on numerical and sensitivity analyses. The results demonstrate that compared with signal-only optimization and the highway capacity methods (HCM), the collaborative optimization method presented in this study reduces the average vehicular delay percentages by 8.54% and 16.47%, respectively. Sensitivity analysis indicates that, under various detour rates and detour modes, collaborative optimization methods have effectively mitigated the average vehicular delay at upstream intersections to varying degrees. In the context of real-time accident response, collaborative optimization methods demonstrate a capacity to promptly address the urgency of incidents occurring and maintain a sustained reduction in overall delay levels following detours.

Evolutionary potential under heat and drought stress at the southern range edge of North American Arabidopsis lyrata.

The warm edges of species' distributions are vulnerable to global warming. Evidence is the recent range retraction from there found in many species. It is unclear why populations cannot easily adapt to warmer, drier, or combined hot and dry conditions and locally persist. Here, we assessed the ability to adapt to these stressors in the temperate species Arabidopsis lyrata. We grew plants from replicate seed families of a central population with high genetic diversity under a temperature and precipitation regime typical of the low-latitude margin or under hotter and/or drier conditions within naturally occurring amplitudes. We then estimated genetic variance-covariance (G-) matrices of traits depicting growth and allocation as well as selection vectors to compare the predicted adaptation potential under the different climate-stress regimes. We found that the sum of genetic variances and genetic correlations were not significantly different under stress as compared to benign conditions. However, under drought and heat drought, the predicted ability to adapt was severely constrained due to strong selection and selection pointing in a direction with less multivariate genetic variation. The much-reduced ability to adapt to dry and hot-dry conditions is likely to reduce the persistence of populations at the low-latitude margin of the species' distribution and contribute to the local extinction of the species under further warming.

Long-term outcomes of pelvic-fascia sparing robotic-assisted radical prostatectomy versus standard technique: Superior urinary function and quality of life without compromising oncologic efficacy in a single-surgeon series

BackgroundProstatic fascia-sparing robotic-assisted radical prostatectomy (PFS-RARP) has improved short-term postoperative continence compared to standard prostatectomy (S-RARP) but long-term differences remain unclear. Materials and MethodsOne hundred two S-RARP followed by 239 PFS-RARPs were performed by a single surgeon. Univariate analyses were performed with t-test, χ2, Wilcoxon rank sum, Fisher exact, and analysis of variance (ANOVA). Regression models analyzed associates of EPIC-CP scores and oncologic outcomes. Cox proportional hazards modeling assessed postoperative continence. Primary outcomes included patient-reported urinary incontinence (UI) via EPIC-CP and continence rates. Secondary outcomes included EPIC-CP scores, positive surgical margins (PSM), and biochemical recurrence (BCR). Perioperative outcomes and time to continence were measured. ResultsMedian follow-up for PFS-RARP vs. S-RARP was 26 vs. 65 months. PFS-RARP demonstrated improved EPIC-CP UI and total scores at 24 months. On multivariate analysis, PFS-RARP was associated with improved EPIC-CP UI and total scores through 18 months, but not with PSM or BCR. PFS-RARP had a 39% and 66% reduced risk of incontinence using 0 and 0 to 1 pad-use definitions (HR 0.61, 95% CI 0.39 – 0.95; HR:0.34, 95% CI 0.16 – 0.76). Continence returned faster with PFS-RARP (0 PPD: 91.0 days vs. 261 days, P < 0.001; 0–1 PPD: 32.7 days vs. 171 days, P < 0.001). There were no differences in PSM (35% vs. 25%, P = 0.064). There were more anterior PSM in PFS-RARP vs. S-RARP (47% vs. 26% P = 0.035), but no differences in BCR (16% vs. 22% P = 0.241). ConclusionsPFS-RARP improves continence and patient-reported QOL up to 24 months postoperatively without compromising oncologic outcomes.

Variance sum rule for entropy production.

Entropy production is the hallmark of nonequilibrium physics, quantifying irreversibility, dissipation, and the efficiency of energy transduction processes. Despite many efforts, its measurement at the nanoscale remains challenging. We introduce a variance sum rule (VSR) for displacement and force variances that permits us to measure the entropy production rate σ in nonequilibrium steady states. We first illustrate it for directly measurable forces, such as an active Brownian particle in an optical trap. We then apply the VSR to flickering experiments in human red blood cells. We find that σ is spatially heterogeneous with a finite correlation length, and its average value agrees with calorimetry measurements. The VSR paves the way to derive σ using force spectroscopy and time-resolved imaging in living and active matter.

Effect of temperature and fluid on rock microstructure based on an effective-medium theory

Abstract Temperature and pressure variations during the geologic diagenesis process can lead to complex pore structures in tight rocks. The effective-medium theory, based on the stress–strain relationship in combination with pore structure parameters, can be used to describe the elastic-wave responses of rocks. In this work, the differential effective medium (DEM) and self-consistent approximation (SCA) models are combined to invert the pore-crack spectrum. The Voigt–Reuss–Hill average is used to estimate the elastic moduli of the minerals. Then, based on SCA, the pore structures are incorporated into the rock matrix to create a new host phase. Subsequently, the DEM theory is used to add cracks with different volume fractions and aspect ratios to the host phase. To predict the structure of pores and cracks (crack density and aspect ratio), an objective function is defined as the sum of variances between experimentally measured and predicted wave velocities. The results show that the modeling predictions of P- and S-wave velocities at different temperatures and pressure ratios agree well with the experimental measurements. Variations in pore structure are determined at a zero effective pressure and different temperatures. We analyze the characteristics of how cracks change with variations in temperature and confining pressure, providing a theoretical basis for characterizing the structure of tight rocks.

Uncertainty relations revisited

Introductory courses on quantum mechanics usually include lectures on uncertainty relations, typically the inequality derived by Robertson and, perhaps, other statements. For the benefit of the lecturers, we present a unified approach — well suited for undergraduate teaching — for deriving all standard uncertainty relations: those for products of variances by Kennard, Robertson, and Schrödinger, as well as those for sums of variances by Maccone and Pati. We also give a brief review of the early history of this topic and try to answer why the use of variances for quantifying uncertainty is so widespread, while alternatives are available that can be more natural and more fitting. It is common to regard the states that saturate the Robertson inequality as “minimum uncertainty states” although they do not minimize the variance of one observable, given the variance of another, incompatible observable. The states that achieve this objective are different and can be found systematically.

Kolmogorov–Smirnov type testing for structural breaks: A new adjusted-range based self-normalization approach

A popular self-normalization (SN) approach in time series analysis uses the variance of a partial sum as a self-normalizer. This is known to be sensitive to irregularities such as persistent autocorrelation, heteroskedasticity, unit roots and outliers. We propose a novel SN approach based on the adjusted-range of a partial sum, which is robust to these aforementioned irregularities. We develop an adjusted-range based Kolmogorov–Smirnov type test for structural breaks for both univariate and multivariate time series, and consider testing parameter constancy in a time series regression setting. Our approach can rectify the well-known power decrease issue associated with existing self-normalized KS tests without having to use backward and forward summations as in Shao and Zhang (2010), and can alleviate the “better size but less power” phenomenon when the existing SN approaches (Shao, 2010; Zhang et al., 2011; Wang and Shao, 2022) are used. Moreover, our proposed tests can cater for more general alternatives. Monte Carlo simulations and empirical studies demonstrate the merits of our approach.

Quantitative radiomics approach to assess acute radiation dermatitis in breast cancer patients.

We applied a radiomics approach to skin surface images to objectively assess acute radiation dermatitis in patients undergoing radiotherapy for breast cancer. A prospective cohort study of 20 patients was conducted. Skin surface images in normal, polarized, and ultraviolet (UV) modes were acquired using a skin analysis device before starting radiotherapy ('Before RT'), approximately 7 days after the first treatment ('RT D7'), on 'RT D14', and approximately 10 days after the radiotherapy ended ('After RT D10'). Eighteen types of radiomic feature ratios were calculated based on the values acquired 'Before RT'. We measured skin doses in ipsilateral breasts using optically stimulated luminescent dosimeters on the first day of radiotherapy. Clinical evaluation of acute radiation dermatitis was performed using the Radiation Therapy Oncology Group scoring criteria on 'RT D14' and 'After RT D10'. Several statistical analysis methods were used in this study to test the performance of radiomic features as indicators of radiodermatitis evaluation. As the skin was damaged by radiation, the energy for normal mode and sum variance for polarized and UV modes decreased significantly for ipsilateral breasts, whereas contralateral breasts exhibited a smaller decrease with statistical significance. The radiomic feature ratios at 'RT D7' had strong correlations to skin doses and those at 'RT D14' and 'after RT D10' with statistical significance. The energy for normal mode and sum variance for polarized and UV modes demonstrated the potential to evaluate and predict acute radiation, which assists in its appropriate management.

Machine learning models and methods for human gait recognition

The paper explores the challenge of human identification through gait recognition within biometric identification systems. Itoutlines the essential criteria for human biometric features, discusses primary biometric characteristics, and their application in biometric identification systems. The paper also examines the feasibility of utilizing gait as a biometric identifier, emphasizing its advantages, such as not requiring the upfront provision of personal biometric information and specialized equipment.The authors conduct an analysis of existing scientific literature in the field of gait recognition, categorizing gait recognition methodsinto template-based and non-template-based approaches. Throughout their research, they identify the key issues and challenges that researchers face in this domain, along with the prevailing trends in human gait recognition within biometric identification systems.Additionally, the paper introduces a method for person identification based on gait, utilizing the Histogram of Oriented Gradients and the Sum Variance Haralick texture features. It involves transforming input video into a series of images depicting the gait silhouette, creating a Gait Energy Image (GEI) by combining these gait silhouettes throughout a gait cycle, and translating the GEI into the Gait Gradient Magnitude Image (GGMI). The subsequent step involves extracting recommended gait characteristics from the GGMIs of participants included in a dataset.To preprocess the collected characteristics, Principal Component Analysis (PCA) is applied, reducing the dimensions that may negatively impact classification robustness, thereby enhancing overall performance. In the final step, a K-Nearest Neighbors (KNN) classifier is employed to categorize the characteristics obtained from a specific dataset.The proposed novel feature vector in the paper demonstrates increased reliability and effectively captures spatial variations in gait patterns. Notably, it reduces the dimensionality of the feature vector from 3780×1 to 63×1, resulting in decreased computational complexity in the gait recognition system. Experimental evaluations on the CASIA A and CASIA B datasets reveal that the proposed approach outperforms other HOG-based methods in most scenarios, with the exception of situations involving frontal images.

Robust topology optimization of truss-like continuum structures under uncertain loads based on cloud model

The traditional structural performance may be seriously degraded due to applied indeterministic cases. A novel framework for robust topology optimization of truss-like continuum considering uncertain loads is presented. The robust optimization formula is constructed as minimizing the weighted sum of expectancy and standard variance of structural compliance with volume constraints. In this framework, both the magnitude and direction of applied loads are uncertain and independent. Truss-like continuum provides the topology optimal structure theoretically. Based on the classic truss-like optimization method, the statistical moments of compliance are accurately evaluated utilizing a bivariate cloud model tool. An effective strategy based on linear decoupling is reported to largely reduce the total number of finite element analysis. The optimization is achieved with gradient-based mathematic programming. Several numerical examples demonstrate a better structural robust performance obtained by the proposed algorithm than the traditional one.

A general framework to develop a radiomic fingerprint for progression-free survival in cervical cancer

PurposeTreatment of locally advanced cervical cancer patients includes chemoradiation followed by brachytherapy. Our aim is to develop a delta radiomics (DRF) model from MRI-based brachytherapy treatment and assess its association with progression free survival (PFS). Materials and MethodsA retrospective analysis of FIGO stage IB- IV cervical cancer patients between 2012 and 2018 who were treated with definitive chemoradiation followed by MRI-based intracavitary brachytherapy was performed. Clinical factors together with 18 radiomic features extracted from different radiomics matrices were analyzed. The delta radiomic features (DRFs) were extracted from MRI on the first and last brachytherapy fractions. Support Vector Machine (SVM) models were fitted to combinations of 2–3 DRFs found significant after Spearman correlation and Wilcoxon rank sum test statistics. Additional models were tested that included clinical factors together with DRFs. ResultsA total of 39 patients were included in the analysis with a median patient age of 52 years. Progression occurred in 20% of patients (8/39). The significant DRFs using two DRF feature combinations was a model using auto correlation (AC) and sum variance (SV). The best performing three feature model combined mean, AC & SV. Additionally, the inclusion of FIGO stages with the 2- and 3 DRF combination model(s) improved performance compared to models with only DRFs. However, all the clinical factor + DRF models were not significantly different from one another (all AUCs were 0.77). ConclusionsOur study shows promising evidence that radiomics metrics are associated with progression free survival in cervical cancer.