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Lung metastasis (LM) status is critical for making treatment decisions in soft-tissue sarcoma (STS) patients, yet magnetic resonance imaging (MRI)-based prediction of LM in STSs has not been thoroughly investigated.This study aimed to develop MRI-based radiomics models for identifying LM in STSs.We enrolled 122 STS patients from our hospital to form a primary cohort. Thirty-two patients from another hospital were included as an external validation cohort. All patients underwent T1-weighted contrast-enhanced (T1-CE) MRI scans before treatment. Radiomics features were extracted from T1-CE MRI sequence and selected by least absolute shrinkage and selection operator (LASSO) to build the radiomics signature. Clinical factors were evaluated using the univariate and multivariate analyses. Multivariable logistic regression analysis was used to construct a clinical-radiomics nomogram incorporating the radiomics signature with margin. Receiver operating characteristic (ROC), calibration and decision curve analysis (DCA) curves were plotted and area under the ROC curves (AUCs) were calculated to assess the predictive performance of nomogram, radiomics signature and margin.A total of five features was finally identified highly related to the LM status to develop the radiomics signature. The nomogram integrating the radiomics signature and margin achieved the best prediction performance in the training (AUCs, nomogram vs. radiomics signature vs. margin, 0.918 vs. 0.894 vs. 0.609), internal validation (AUCs, nomogram vs. radiomics signature vs. margin, 0.864 vs. 0.841 vs. 0.666) and external validation (AUCs, nomogram vs. radiomics signature vs. margin, 0.843 vs. 0.800 vs. 0.643) sets.The developed nomogram was a promising tool to help make preoperative treatment strategies for STSs.

To compare values of multiparametric magnetic resonance imaging (MRI) sequences and propose clinical-radiomics nomogram for diagnosis of soft-tissue sarcoma (STS).This study enrolled 148 patients from Dec. 2017 to Feb. 2021. All patients underwent T1-weighted (T1W), contrast-enhanced T1-weighted (T1CE), and T2-weighted fat-suppressed (T2FS) MRI scans. A total of 1967 radiomic features were extracted from the segmented regions of interest (ROIs) in each MRI sequence. Highly diagnostic radiomic features were selected with Mann-Whitney U test, elastic net, and Akaike's information criterion (AIC) based on MRI images. Logistical regression was used to build Rad scores. Clinical factors were analyzed using the chi-square test or Mann-Whitney U test. The performance of the Rad scores was judged using the area under the receiver operating characteristic area under the curve (ROC AUC), sensitivity, specificity, and accuracy. The nomogram was developed by integrating the Rad score and the most important clinical factor.By combining the three MRI sequences, the Rad-Com was developed consisting of twelve features selected by with Mann-Whitney U test, elastic net, and AIC: four from T1W, three from TICE, and five from T2FS MRI. The margin (P 0.05) demonstrated a statistically significant difference between patients with benign and malignant soft-tissue tumors (STT). The nomogram was constructed by integrating the Rad-Com and margin, which yielded favorable diagnostic AUCs of 0.919 (sensitivity (Sen) = 0.784, specificity (Spe) = 0.936) and 0.913 (Sen = 0.923, Spe = 0.792) in the training and validation cohort.The proposed nomogram may have potential as a noninvasive marker for STS diagnosis.

A acetic acid mediated 1,10-conjugate addition-mediated formal [3 + 3] cyclization of alkynyl indole imine methides formed in situ from α-(6-indolyl) propargylic alcohols with 1,3-dicarbonyl compounds such as 4-hydroxycoumarins and cyclohexane-1,3-diones was developed.

To address the low Ca−induced growth inhibition of tomato plants, the mitigation effect of exogenous Si on tomato seedlings under low−Ca stress was investigated using different application methods. We specifically analyzed the effects of root application or foliar spraying of 1 mM Si on growth conditions, leaf photosynthetic properties, stomatal status, chlorophyll content, chlorophyll fluorescence, ATP activity and content, Calvin cycle−related enzymatic activity, and gene expression in tomato seedlings under low vs. adequate calcium conditions. We found that the low−Ca environment significantly affected (reduced) these parameters, resulting in growth limitation. Surprisingly, the application of 1 mM Si significantly increased plant height, stem diameter, and biomass accumulation, protected photosynthetic pigments, improved gas exchange, promoted ATP production, enhanced the activity of Calvin cycle key enzymes and expression of related genes, and ensured efficient photosynthesis to occur in plants under low−Ca conditions. Interestingly, when the same amount of Si was applied, the beneficial effects of Si were more pronounced under low−Ca conditions that under adequate Ca. We speculate that Si might promote the absorption and transport of calcium in plants. The effects of Si also differed depending on the application method; foliar spraying was better in alleviating photosynthetic inhibition in plants under low−Ca stress, whereas root application of Si significantly promoted root growth and development. Enhancing the photosynthetic capacity by foliar Si application is an effective strategy for ameliorating the growth inhibition of plants under low−Ca stress.

Purpose To develop a multi-modality MRI-based radiomics nomogram for predicting the lung metastasis (LM) in soft-tissue sarcoma (STS). Methods We enrolled 122 patients who clinicopathologically confirmed STS from our hospital to form a primary cohort. Thirty-two patients from another hospital were included as an external validation cohort. All patients underwent pretreatment T1-weighted contrast-enhanced (T1-CE) MRI scans. Radiomics features were calculated and selected from the T1-CE MRI sequence, and used to build the radiomics signature. Clinical factors were evaluated by the logistic regression. Multivariable logistic regression analysis was applied to construct a radiomics nomogram incorporating the radiomics signature with the important clinical factor. Receiver operating characteristic (ROC), calibration and decision curve analysis (DCA) curves were plotted to assess the radiomics methods. Results A total of 5 features were finally identified highly related to the LM status to develop the radiomics signature. A clinical-radiomics nomogram integrating the radiomics signature and margin achieved the best prediction performance in the training (AUCs, nomogram vs. radiomics signature vs. margin, 0.918 vs. 0.864 vs. 0.609), internal validation (AUCs, nomogram vs. radiomics signature vs. margin, 0.864 vs. 0.841 vs. 0.666) and external validation (AUCs, nomogram vs. radiomics signature vs. margin, 0.843 vs. 0.800 vs. 0.643) cohort. DCA indicated potential usefulness of the nomogram. Conclusions This study evaluated predictive values of T1-CE MRI for the prediction of lung metastasis in STSs, and proposed a nomogram model to potentially facilitate the preoperative individualized treatment decision making for STSs.

Background: To develop a multi-modality MRI-based radiomics nomogram for predicting the lung metastasis (LM) in soft-tissue sarcoma (STS).Methods: We enrolled 122 patients who clinicopathologically confirmed STS from our hospital to form a primary cohort. Thirty-two patients from another hospital were included as an external validation cohort. All patients underwent pretreatment T1-weighted contrast-enhanced (T1-CE) MRI scans. Radiomics features were calculated and selected from the T1-CE MRI sequence, and used to build the radiomics signature. Clinical factors were evaluated by the logistic regression. Multivariable logistic regression analysis was applied to construct a radiomics nomogram incorporating the radiomics signature with the important clinical factor. Receiver operating characteristic (ROC), calibration and decision curve analysis (DCA) curves were plotted to assess the radiomics methods.Results: A total of 5 features were finally identified highly related to the LM status to develop the radiomics signature. A clinical-radiomics nomogram integrating the radiomics signature and margin achieved the best prediction performance in the training (AUCs, nomogram vs. radiomics signature vs. margin, 0.918 vs. 0.864 vs. 0.609), internal validation (AUCs, nomogram vs. radiomics signature vs. margin, 0.864 vs. 0.841 vs. 0.666) and external validation (AUCs, nomogram vs. radiomics signature vs. margin, 0.843 vs. 0.800 vs. 0.643) cohort. DCA indicated potential usefulness of the nomogram.Conclusions: This study evaluated predictive values of T1-CE MRI for the prediction of lung metastasis in STSs, and proposed a nomogram model to potentially facilitate the preoperative individualized treatment decision making for STSs.

PURPOSE To non-invasively evaluate the Ki-67 level in digital breast tomosynthesis (DBT) images of breast cancer (BC) patients based on subregional radiomics. METHODS A total of 266 patients who underwent DBT scans were consecutively enrolled at two centers, between September 2017 and September 2021. The whole tumor region was partitioned into various intratumoral subregions, based on individual- and population-level clustering. Handcrafted radiomics features and deep learning-based features were extracted from the subregions and from the whole tumor region, and were selected by least absolute shrinkage and selection operator (LASSO) regression, yielding radiomics signatures (RSs). The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were calculated to assess the developed RSs. RESULTS Each breast tumor region was partitioned into an inner subregion (S1) and a marginal subregion (S2). The RSs derived from S1 always generated higher AUCs compared with those from S2 or from the whole tumor region (W), for the external validation cohort (AUCs, S1 vs. W, handcrafted RSs: 0.583 [95% CI, 0.429-0.727] vs. 0.559 [95% CI, 0.405-0.705], P value: 0.920; deep RSs: 0.670 [95% CI, 0.516-0.802] vs. 0.551 [95% CI, 0.397-0.698], P value: 0.776). The fusion RSs, combining handcrafted and deep learning-based features derived from S1, yielded the highest AUCs of 0.820 (95% CI, 0.714-0.900) and 0.792 (95% CI, 0.647-0.897) for the internal and external validation cohorts, respectively. CONCLUSIONS The subregional radiomics approach can accurately predict the Ki-67 level based on DBT data; thus, it may be used as a potential non-invasive tool for preoperative treatment planning in BC. This article is protected by copyright. All rights reserved.

To develop and validate a multiparametric magnetic resonance imaging-based radiomics nomogram for differentiating malignant and benign soft-tissue tumors.A total of 91 patients with pathologically confirmed soft-tissue tumors were enrolled between January 2017 and October 2020. Forty-eight patients were consecutively enrolled between November 2020 and March 2022, as a time-independent cohort. All patients underwent contrast-enhanced T1-weighted and T2-weighted fat-suppression magnetic resonance scans at 3.0 T. Radiomics features were extracted and selected from the two modalities to develop the radiomics signature. Significant clinical/morphological characteristics were identified using a multivariate logistic regression analysis. The least absolute shrinkage and selection operator regression were applied to identify discriminative features. A clinical-radiomics nomogram was constructed based on clinical/morphological characteristics and radiomics features. Finally, the performance of the nomogram was validated using the receiver operating characteristic and decision curve analysis (DCA).Six features were selected to establish the combined RS. Size, margin, and peritumoral edema were identified as the most important clinical and morphological factors, respectively. The radiomics signature outperformed the clinical model in terms of AUC and sensitivity. The nomogram integrating the combined RS, size, margin, and peritumoral edema achieved favorable predictive efficacy, generating AUCs of 0.954 (95% confidence interval [CI]: 0.907-1.000, Sen = 0.861, Spe = 0.917), 0.962 (95% CI: 0.901-1.000, Sen = 0.944, Spe = 0.923), and 0.935 (95% CI: 0.871-0.998, Sen = 0.815, Spe = 0.952) in the training (n = 60), validation (n = 31) and time-independent (n = 48) cohorts, respectively. The DCA curve indicated good clinical usefulness of the nomogram.Our study demonstrated the clinical potential of multiparametric MRI-based radiomics in distinguishing malignant from benign soft-tissue tumors, which can be considered as a noninvasive tool for individual treatment management.

Objective. To develop and externally validate habitat-based MRI radiomics for preoperative prediction of the EGFR mutation status based on brain metastasis (BM) from primary lung adenocarcinoma (LA). Approach. We retrospectively reviewed 150 and 38 patients from hospital 1 and hospital 2 between January 2017 and December 2021 to form a primary and an external validation cohort, respectively. Radiomics features were calculated from the whole tumor (W), tumor active area (TAA) and peritumoral oedema area (POA) in the contrast-enhanced T1-weighted (T1CE) and T2-weighted (T2W) MRI image. The least absolute shrinkage and selection operator was applied to select the most important features and to develop radiomics signatures (RSs) based on W (RS-W), TAA (RS-TAA), POA (RS-POA) and in combination (RS-Com). The area under receiver operating characteristic curve (AUC) and accuracy analysis were performed to assess the performance of radiomics models. Main results. RS-TAA and RS-POA outperformed RS-W in terms of AUC, ACC and sensitivity. The multi-region combined RS-Com showed the best prediction performance in the primary validation (AUCs, RS-Com versus RS-W versus RS-TAA versus RS-POA, 0.901 versus 0.699 versus 0.812 versus 0.883) and external validation (AUCs, RS-Com versus RS-W versus RS-TAA versus RS-POA, 0.900 versus 0.637 versus 0.814 versus 0.842) cohort. Significance. The developed habitat-based radiomics models can accurately detect the EGFR mutation in patients with BM from primary LA, and may provide a preoperative basis for personal treatment planning.