Interplay between edaphic and climatic factors unravels plant and microbial diversity along an altitudinal gradient.

Altitude influences biodiversity and physiochemical soil attributes in terrestrial ecosystems. It is of immense importance to know the patterns of how interactions among climatic and edaphic factors influence plant and microbial diversity in various ecosystems, particularly along the gradients. We hypothesize that altitudinal variation determines the distribution of plant and microbial species as well as their interactions. To test the hypothesis, different sites with variable altitudes were selected. Analyses of edaphic factors revealed significant (p < 0.001) effects of the altitude. Soil ammonium and nitrate were strongly affected by it contrary to potassium (K), soil organic matter and carbon. The response patterns of individual taxonomic groups differed across the altitudinal gradient. Plant species and soil fungal diversity increased with increasing altitude, while soil archaeal and bacterial diversity decreased with increasing altitude. Plant species richness showed significant positive and negative interactions with edaphic and climatic factors. Fungal species richness was also significantly influenced by the soil ammonium, nitrate, available phosphorus, available potassium, electrical conductivity, and the pH of the soil, but showed non-significant interactions with other edaphic factors. Similarly, soil variables had limited impact on soil bacterial and archaeal species richness along the altitude gradient. Proteobacteria, Ascomycota, and Thaumarchaeota dominate soil bacterial, fungal, and archaeal communities, with relative abundance of 27.4%, 70.56%, and 81.55%, respectively. Additionally, Cynodon dactylon is most abundant plant species, comprising 22.33% of the recorded plant taxa in various study sites. RDA revealed that these communities influenced by certain edaphic and climatic factors, e.g., Actinobacteria strongly respond to MAT, EC, and C/N ratio, Ascomycota and Basidiomycota show strong associations with EC and MAP, respectively. Thaumarcheota are linked to pH, and OM, while Cyperus rotundus are sensitive to AI and EC. In conclusion, the observed variations in microbial as well as plant species richness and changes in soil properties at different elevations provide valuable insights into the factors determining ecosystem stability and multifunctionality in different regions.

Heavy metal contamination impacts the structure and co-occurrence patterns of bacterial communities in agricultural soils.

Heavy metal (HM) contamination caused by mining and smelting activities can be harmful to soil microbiota, which are highly sensitive to HM stress. Here, we explore the effects of HM contamination on the taxonomic composition, predicted function, and co-occurrence patterns of soil bacterial communities in two agricultural fields with contrasting levels of soil HMs (i.e., contaminated and uncontaminated natural areas). Our results indicate that HM contamination does not significantly influence soil bacterial α diversity but changes the bacterial community composition by enriching the phyla Gemmatimonadetes, Planctomycetes, and Parcubacteria and reducing the relative abundance of Actinobacteria. Our results further demonstrate that HM contamination can strengthen the complexity and modularity of the bacterial co-occurrence network but weaken positive interactions between keystone taxa, leading to the gradual disappearance of some taxa that originally played an important role in healthy soil, thereby possibly reducing the resistance of bacterial communities to HM toxicity. The predicted functions of bacterial communities are related to membrane transport, amino acid metabolism, energy metabolism, and carbohydrate metabolism. Among these, functions related to HM detoxification and antioxidation are enriched in uncontaminated soils, while HM contamination enriches functions related to metal resistance. This study demonstrated that microorganisms adapt to the stress of HM pollution by adjusting their composition and enhancing their network complexity and potential ecological functions.

Plant trait networks reveal adaptation strategies in the drylands of China.

BACKGROUND: Plants accomplish multiple functions by the interrelationships between functional traits. Clarifying the complex relationships between plant traits would enable us to better understand how plants employ different strategies to adapt to the environment. Although increasing attention is being paid to plant traits, few studies focused on the adaptation to aridity through the relationship among multiple traits. We established plant trait networks (PTNs) to explore the interdependence of sixteen plant traits across drylands. RESULTS: Our results revealed significant differences in PTNs among different plant life-forms and different levels of aridity. Trait relationships for woody plants were weaker, but were more modularized than for herbs. Woody plants were more connected in economic traits, whereas herbs were more connected in structural traits to reduce damage caused by drought. Furthermore, the correlations between traits were tighter with higher edge density in semi-arid than in arid regions, suggesting that resource sharing and trait coordination are more advantageous under low drought conditions. Importantly, our results demonstrated that stem phosphorus concentration (SPC) was a hub trait correlated with other traits across drylands. CONCLUSIONS: The results demonstrate that plants exhibited adaptations to the arid environment by adjusting trait modules through alternative strategies. PTNs provide a new insight into understanding the adaptation strategies of plants to drought stress based on the interdependence among plant functional traits.

A simple method to import CAD mesh format models in FLUKA.

BACKGROUND: Monte Carlo (MC) code FLUKA possesses widespread usage and accuracy in the simulation of particle beam radiotherapy. However, the conversion from computer-aided design (CAD) mesh format models to FLUKA readable geometries could not be implemented directly and conveniently. A simple method was required to be developed. PURPOSE: The present study proposed a simple method to voxelize CAD mesh format files by using a Python-based script and establishing geometric models in FLUKA. METHODS: Five geometric models including cube, sphere, cone, ridge filter (RGF), and 1D-Ripple Filter (1D-RiFi) were created and exported as CAD mesh format files (.stl). An open-source Python-based script was used to convert them into voxels by endowing X, Y, and Z (following the Cartesian coordinates system) of solid materials in the three-dimensional (3D) grid. A FLUKA (4-2.2, CERN) predefined routine was used to establish the voxelized geometry model (VGM), while Flair (3.2-1, CERN) was used to build the direct geometry model (DGM) in FLUKA for comparison purposes. Uniform carbon ion radiation fields 8×8 cm3 and 4×4 cm3 were generated to transport through the five pairs of models, 2D and 3D dose distributions were compared. The integral depth dose (IDD) in water of three different energy levels of carbon ion beams transported through 1D-RiFis were also simulated and compared. Moreover, the volume between CAD mesh and VGMs, as well as the computing speed between FLUKA DGMs and VGMs were simultaneously recorded. RESULTS: The volume differences between VGMs and CAD mesh models were not more than 0.6%. The maximum mean point-to-point deviation of IDD distribution was 0.7% ± 0.51% (mean ± standard deviation). The 3D dose Gamma-index passing rates were never lower than 97% with criteria of 1%-1 mm. The difference in computing CPU time was 2.89% ± 0.22 on average. CONCLUSIONS: The present study proposed and verified a Python-based method for converting CAD mesh format files into VGMs and establishing them in FLUKA simply as well as accurately.

Geometric and dosimetric evaluation of deep learning based auto-segmentation for clinical target volume on breast cancer.

BACKGROUND: Recently, target auto-segmentation techniques based on deep learning (DL) have shown promising results. However, inaccurate target delineation will directly affect the treatment planning dose distribution and the effect of subsequent radiotherapy work. Evaluation based on geometric metrics alone may not be sufficient for target delineation accuracy assessment. The purpose of this paper is to validate the performance of automatic segmentation with dosimetric metrics and try to construct new evaluation geometric metrics to comprehensively understand the dose-response relationship from the perspective of clinical application. MATERIALS AND METHODS: A DL-based target segmentation model was developed by using 186 manual delineation modified radical mastectomy breast cancer cases. The resulting DL model were used to generate alternative target contours in a new set of 48 patients. The Auto-plan was reoptimized to ensure the same optimized parameters as the reference Manual-plan. To assess the dosimetric impact of target auto-segmentation, not only common geometric metrics but also new spatial parameters with distance and relative volume ( R V ${R}_V$ ) to target were used. Correlations were performed using Spearman's correlation between segmentation evaluation metrics and dosimetric changes. RESULTS: Only strong (|R2 | > 0.6, p < 0.01) or moderate (|R2 | > 0.4, p < 0.01) Pearson correlation was established between the traditional geometric metric and three dosimetric evaluation indices to target (conformity index, homogeneity index, and mean dose). For organs at risk (OARs), inferior or no significant relationship was found between geometric parameters and dosimetric differences. Furthermore, we found that OARs dose distribution was affected by boundary error of target segmentation instead of distance and R V ${R}_V$ to target. CONCLUSIONS: Current geometric metrics could reflect a certain degree of dose effect of target variation. To find target contour variations that do lead to OARs dosimetry changes, clinically oriented metrics that more accurately reflect how segmentation quality affects dosimetry should be constructed.

Reactivity and Recyclability of Ligand-Protected Metal Cluster Catalysts for CO2 Transformation.

It remains a significant challenge to construct an integrated catalyst that combines advantages of homogeneous and heterogeneous catalysis with clarified mechanism and high performance. Here we show atomically precise CuAg cluster catalysts for CO2 capture and utilization, where two functional units are combined into the clusters: metal and ligand. Due to atomic resolution on total and local structures of such catalysts to be achieved, which disentangles heterogeneous imprecise systems and permits tracing the reaction processes via experiments coupled with theory, site-specific catalysis induced by metal-ligand synergy can be accurately elucidated. The CuAg cluster catalysts exhibit excellent reactivity and recyclability to forge the C-N bonding from CO2 formylation with secondary amines that can make the cluster catalysts more unique compared with typically homogeneous complexes.

[Dosimetric Impact of Titanium Alloy Implant in Spinal IMRT Plan].

The purpose of this study is to establish and apply a correction method for titanium alloy implant in spinal IMRT plan, a corrected CT-density table was revised from normal CT-density table to include the density of titanium alloy implant. Dose distribution after and before correction were calculated and compared to evaluate the dose deviation. Plans were also copied to a spinal cancer simulation phantom. A titanium alloy fixation system for spine was implanted in this phantom. Plans were recalculated and compared with the measurement result. The result of this study shows that the max dose of spinal cord showed significant difference after correction, and the deviation between calculation results and measurement results was reduced after correction. The method for expanding the range CT-density table, which means that the density of titanium alloy was included, can reduce the error in calculation.

Slope aspect determines the abundance and composition of nitrogen-cycling microbial communities in an alpine ecosystem.

Slope aspect is an important topographic feature that can influence local environmental conditions. While strong effects of slope aspect on aboveground and belowground communities have been frequently elucidated, how slope aspect affects soil nitrogen (N) cycling microbes remains unclear. Here, we characterized the communities of soil N-cycling microbes on south- and north-facing slopes in an alpine ecosystem, by quantifying (qPCR) and high-throughput sequencing six genes involved in N-fixation (nifH), nitrification (archaeal and bacterial amoA) and denitrification (nirK, nirS and nosZ). We found that the abundance, diversity and community composition of major N-cycling microbes differed dramatically between the two slope aspects, and these variances could be well explained by the aspect-driven differences in environmental conditions, especially soil temperature and moisture. The response patterns of different N-cycling groups to slope aspect were much inconsistent, especially for those with similar functions (i.e. ammonia-oxidizing archaea vs. bacteria, nirK- vs. nirS-reducers), indicating strong niche differentiation between these counterparts. We also observed strong preferences and distinct co-occurrence patterns of N-cycling microbial taxa for the two slope aspects. These findings highlight the importance of slope aspect in determining the abundance, species distribution and community structure of N-cycling microbes, and consequently influencing N-cycling processes and ecosystem functioning.

Continental-scale niche differentiation of dominant topsoil archaea in drylands.

Archaea represent a diverse group of microorganisms often associated with extreme environments. However, an integrated understanding of biogeographical patterns of the specialist Haloarchaea and the potential generalist ammonia-oxidizing archaea (AOA) across large-scale environmental gradients remains limited. We hypothesize that niche differentiation determines their distinct distributions along environmental gradients. To test the hypothesis, we use a continental-scale research network including 173 dryland sites across northern China. Our results demonstrate that Haloarchaea and AOA dominate topsoil archaeal communities. As hypothesized, Haloarchaea and AOA show strong niche differentiation associated with two ecosystem types mainly found in China's drylands (i.e. deserts vs. grasslands), and they differ in the degree of habitat specialization. The relative abundance and richness of Haloarchaea are higher in deserts due to specialization to relatively high soil salinity and extreme climates, while those of AOA are greater in grassland soils. Our results further indicate a divergence in ecological processes underlying the segregated distributions of Haloarchaea and AOA. Haloarchaea are governed primarily by environmental-based processes while the more generalist AOA are assembled mostly via spatial-based processes. Our findings add to existing knowledge of large-scale biogeography of topsoil archaea, advancing our predictive understanding on changes in topsoil archaeal communities in a drier world.

Multimodal image translation via deep learning inference model trained in video domain.

BACKGROUND: Current medical image translation is implemented in the image domain. Considering the medical image acquisition is essentially a temporally continuous process, we attempt to develop a novel image translation framework via deep learning trained in video domain for generating synthesized computed tomography (CT) images from cone-beam computed tomography (CBCT) images. METHODS: For a proof-of-concept demonstration, CBCT and CT images from 100 patients were collected to demonstrate the feasibility and reliability of the proposed framework. The CBCT and CT images were further registered as paired samples and used as the input data for the supervised model training. A vid2vid framework based on the conditional GAN network, with carefully-designed generators, discriminators and a new spatio-temporal learning objective, was applied to realize the CBCT-CT image translation in the video domain. Four evaluation metrics, including mean absolute error (MAE), peak signal-to-noise ratio (PSNR), normalized cross-correlation (NCC), and structural similarity (SSIM), were calculated on all the real and synthetic CT images from 10 new testing patients to illustrate the model performance. RESULTS: The average values for four evaluation metrics, including MAE, PSNR, NCC, and SSIM, are 23.27 ± 5.53, 32.67 ± 1.98, 0.99 ± 0.0059, and 0.97 ± 0.028, respectively. Most of the pixel-wise hounsfield units value differences between real and synthetic CT images are within 50. The synthetic CT images have great agreement with the real CT images and the image quality is improved with lower noise and artifacts compared with CBCT images. CONCLUSIONS: We developed a deep-learning-based approach to perform the medical image translation problem in the video domain. Although the feasibility and reliability of the proposed framework were demonstrated by CBCT-CT image translation, it can be easily extended to other types of medical images. The current results illustrate that it is a very promising method that may pave a new path for medical image translation research.

Commissioning of and preliminary experience with a new fully integrated computed tomography linac.

PURPOSE: A new medical linear accelerator (linac) platform integrated with helical computed tomography (CT), the uRT-linac 506c, was introduced into clinical application in 2019 by United Imaging Healthcare (UIH) Co., Ltd. (Shanghai, China). It combines a Carm linac with a diagnostic-quality 16-slice CT imager, providing seamless workflow from simulation to treatment. The aim of this report is to assess the technical characteristics, commissioning results and preliminary experiences stemming from clinical usage. METHODS: The mechanical and imaging test procedures, commissioning data collection and TPS validation were summarized. CTIGRT accuracy was investigated with different loads and couch extensions. A series of end-to-end cases for different treatment sites and delivery techniques were tested preclinically to estimate the overall accuracy for the entire treatment scheme. The results of patient-specific QA and machine stability during a one-year operation are also reported. RESULTS: Gantry/couch/collimator isocentricity was measured as 0.63 mm in radius. The TPS models were in agreement with the beam commissioning data within a deviation of 2%. An overall submillimeter accuracy was demonstrated for the CT-IGRT process under all conditions. The absolute point dose difference for all the preclinical end-to-end tests was within 3%, and the gamma passing rate of the 2D dose distribution measured by EBT3 film was better than 90% (3% DD, 3 mm DTA and 10% threshold). Pretreatment QA of clinical cases resulted with better than 3% point dose difference and more than 99% gamma passing rate (3% DD/2 mm DTA/10% threshold) tested with Delta4. The output of the linac was mostly within 1% of variation in a one-year operation. CONCLUSION: The commissioning results and clinical QA results show that the uRT-linac 506c platform exhibits good and stable performance in mechanical and dosimetric accuracy. The integrated CT system provides an efficient workflow for image guidance with submillimeter localization precision, and will be a good starting point to proceed advanced adaptive radiotherapy.

Precisely Constructed Silver Active Sites in Gold Nanoclusters for Chemical Fixation of CO2.

Precise control of the composition and structure of active sites in an atom-by-atom fashion remains insuperable for heterogeneous catalysts. Here, we introduce tailor-made catalytic sites for the cycloaddition of CO2 to epoxides achieved by implementing Ag atoms at different levels of liberation in atomically precise Au nanoclusters. Our results reveal that a single open Ag site on the Au19 Ag4 cluster improves the ring-opening of epoxides and sequent CO2 insertion, while the partially exposed Ag site on the Au20 Ag1 cluster exhibits a weak affinity for epoxides and poor efficiency for CO2 capture. Structural tunability imparted by the atom-by-atom tailoring and unusual atomic charges distributed on Au and Ag atoms of the three clusters seem to be crucial for promoting challenging bond cleavages and formations in the chemical utilization of CO2 .

Structural Relaxation Enabled by Internal Vacancy Available in a 24-Atom Gold Cluster Reinforces Catalytic Reactivity.

Unveiling the mystery of the contribution of nonsurface or noninterface sites in a catalyst to its catalytic performance remains a great challenge because of the difficulty in capturing precisely structural information (surface plus inner) encoded in the catalyst. This work attempts to elucidate the critical role of the internal vacancy in an atomically precise 24-atom gold cluster in regulating the catalytic performance on the hydrogenation reaction of CO2. The experiment results show that the Au24 cluster with internal vacancy can mitigate sintering and exhibit high catalytic activity under relatively harsh reaction conditions, in contrast to the structurally similar Au25 cluster without internal vacancy. Our computational study suggests that the internal vacancy in Au24 provides the cluster with much more structural flexibility, which may be crucial to resisting the aggregation of the cluster and further postponing the deactivation. The hydrogenation and coupling stages of the reaction intermediates are proposed to explain the potential reaction pathway of CO2 with H2 on the Au24 catalyst with internal vacancy.

Water content quantitatively affects metabolic rates over the course of plant ontogeny.

Plant metabolism determines the structure and dynamics of ecological systems across many different scales. The metabolic theory of ecology quantitatively predicts the scaling of metabolic rate as a function of body size and temperature. However, the role of tissue water content has been neglected even though hydration significantly affects metabolism, and thus ecosystem structure and functioning. Here, we use a general model based on biochemical kinetics to quantify the combined effects of water content, body size and temperature on plant metabolic rates. The model was tested using a comprehensive dataset from 205 species across 10 orders of magnitude in body size from seeds to mature large trees. We show that water content significantly influences mass-specific metabolic rates as predicted by the model. The scaling exponents of whole-plant metabolic rate vs body size numerically converge onto 1.0 after water content is corrected regardless of body size or ontogenetic stage. The model provides novel insights into how water content together with body size and temperature quantitatively influence plant growth and metabolism, community dynamics and ecosystem energetics.

An Au8 Cluster Fortified by Four Ferrocenes.

Atomically precise metal clusters are now in the research spotlight, owing to the precise correlation between the physicochemical properties and their atomic-packing structures at an atomic-level. Herein we synthesized an Au8 cluster capped by four ferrocene ligands (DPPF), in which the ferrocene not only can direct the precise formation of the Au8 cluster, but also can solidify the structural pattern of the Au8 cluster. The Au8(DPPF)4 clusters as heterogeneous catalysts can achieve efficiently catalytic performances for the CO oxidation reaction, mainly due to the resistance to aggregation into large particles under reaction conditions. Our results suggest that the homolytic phosphine dissociation nature and the postdissociation reconstruction effect induced by Fe may enhance the catalytic performances of Au8(DPPF)4.

The benefits evaluation of abdominal deep inspiration breath hold based on knowledge-based radiotherapy treatment planning for left-sided breast cancer.

PURPOSE: To study the impact of abdominal deep inspiration breath hold (DIBH) technique on knowledge-based radiotherapy treatment planning for left-sided breast cancer to guide the application of DIBH technology. MATERIALS AND METHODS: Two kernel density estimation (KDE) models were developed based on 40 left-sided breast cancer patients with two CT acquisitions of free breathing (FB-CT) and DIBH (DIBH-CT). Each KDE model was used to predict dose volume histograms (DVHs) based on DIBH-CT and FB-CT for another 10 new patients similar to our training datasets. The predicted DVHs were taken as a substitute for dose constraints and objective functions in the Eclipse treatment planning system, with the same requirements for the planning target volume (PTV). The mean doses to the heart, the left anterior descending coronary artery (LADCA) and the ipsilateral lung were evaluated and compared using the T-test among clinical plans, KDE predictions, and KDE plans. RESULTS: Our study demonstrated that the KDE model can generate deliverable simulations equivalent to clinically applicable plans. The T-test was applied to test the consistency hypothesis on another ten left-sided breast cancer patients. In cases of the same breathing status, there was no statistically significant difference between the predicted and the clinical plans for all clinically relevant DVH indices (P > 0.05), and all predicted DVHs can be transferred into deliverable plans. For DIBH-CT images, significant differences were observed between FB model predictions and clinical plans (P < 0.05). DIBH model prediction cannot be optimized to a deliverable plan based on FB-CT, with a counsel of perfection. CONCLUSION: KDE models can predict DVHs well for the same breathing conditions but degrade with different breathing conditions. The benefits of DIBH for a given patient can be evaluated with a quick comparison of prediction results of the two models before treatment planning.

Reactivity and Lability Modulated by a Valence Electron Moving in and out of 25-Atom Gold Nanoclusters.

The emergence of atomically precise metal nanoclusters with unique electronic structures provides access to currently inaccessible catalytic challenges at the single-electron level. We investigate the catalytic behavior of gold Au25 (SR)18 nanoclusters by monitoring an incoming and outgoing free valence electron of Au 6s1 . Distinct performances are revealed: Au25 (SR)18 - is generated upon donation of an electron to neutral Au25 (SR)18 0 and this is associated with a loss in reactivity, whereas Au25 (SR)18 + is generated from dislodgment of an electron from neutral Au25 (SR)18 0 with a loss in stability. The reactivity diversity of the three Au25 (SR)18 clusters stems from different affinities with reactants and the extent of intramolecular charge migration during the reactions, which are closely associated with the valence occupancies of the clusters varied by one electron. The stability difference in the three clusters is attributed to their different equilibria, which are established between the AuSR dissociation and polymerization influenced by one electron.

Implement a knowledge-based automated dose volume histogram prediction module in Pinnacle3 treatment planning system for plan quality assurance and guidance.

PURPOSE: This work aims to develop a knowledge-based automated dose volume histogram (DVH) prediction module that serves as a plan quality evaluation tool and treatment planning guidance in commercial Pinnacle3 treatment planning system (Philips Radiation Oncology Systems, Fitchburg, WI, USA). METHODS: The knowledge-based automated DVH prediction module was developed with kernel density estimation (KDE) method and applied for Pinnacle3 treatment planning system. Treatment plan data from 20 esophageal cancer cases were used for creating a module to predict DVHs. Twenty additional esophageal clinical plans were evaluated on the developed module. Predicted DVHs were compared with manual ones. Differences between the predicted and achieved DVHs were analyzed. RESULTS: The plan evaluation module was successfully implemented in Pinnacle3 treatment planning system. Strong linear correlations were found between predicted and achieved DVH for organs at risk. Suboptimal treatment plan quality could be improved according to the predicted DVHs by the module. CONCLUSION: The knowledge-based automated DVH prediction module implemented in Pinnacle3 could be used to efficiently evaluate the treatment plan quality and as guidance for further plan optimization.

[Study on the Quick Daily Check for Medical Electron LINAC].

This study presents an electronic portal imaging devices (EPIDs) based on daily check tool for Linac that is usable for different cancer centers.Several images of open rectangle fields were acquired with EPID and the key items of daily Linac check were derived from the obtained images using an in-house developed automatic analysis software.The experiment results showed that each parameter calculated by this tool is as reliable as the corresponding result measured by the commercial quality assurance devices and its measuring efficiency is much higher.

Radiomic features of pretreatment MRI could identify T stage in patients with rectal cancer: Preliminary findings.

BACKGROUND: Recent studies have shown that magnetic resonance (MR) radiomic analysis is feasible and has some value in identifying tumor characteristics, but there are few data regarding the role of MR-based radiomic features in rectal cancer. PURPOSE: The aim of this study was to determine whether radiomic features extracted from T2 -weighted imaging (T2 WI) can identify pathological features in rectal cancer. STUDY TYPE: Retrospective study. POPULATION/SUBJECTS: A cohort comprising 119 rectal cancer patients who underwent surgery between January 2015 and November 2016. FIELD STRENGTH/SEQUENCE: 3.0T, axial high-resolution T2 -weighted turbo spin echo (TSE) sequence. ASSESSMENT: Patients were classified according to pathological features such as T stage, N stage, perineural invasion, histological grade, lymph-vascular invasion, tumor deposits, and circumferential resection margin (CRM). The whole tumor volume (WTV) was distinguished, and segments were quantified on axial high-resolution T2 WI by a radiologist. A total of 256 radiomic features were extracted. STATISTICAL TESTS: To achieve reliable results, cluster analysis and least absolute shrinkage and selection operator (LASSO) were implemented. In the cluster analysis, the patients were divided into two groups, and chi-square tests were performed to investigate the relationship between the pathological features and the radiomic-based clusters. The area under the curve (AUC) was calculated to evaluate the predictability of the model in the LASSO analysis. RESULTS: The cluster results revealed that patients could be stratified into two groups, and the chi-square test results indicated that the pT stage was correlated with the radiomic feature cluster results (P = 0.002). The prediction model AUC for the diagnostic T stage was 0.852 (95% confidence interval: 0.677-1; sensitivity: 79.0%, specificity: 82.0%). DATA CONCLUSION: The use of MRI-derived radiomic features to identify the T stage is feasible in rectal cancer. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.