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Histopathological grading is a significant risk factor for postsurgical recurrence in hepatocellular carcinoma (HCC). Preoperative knowledge of histopathological grading could provide instructive guidance for individualized treatment decision-making in HCC management.This study aims to develop and validate a newly proposed deep learning model to predict histopathological grading in HCC with improved accuracy.In this dual-center study, we retrospectively enrolled 384 HCC patients with complete clinical, pathological and radiological data. Aiming to synthesize radiological information derived from both tumor parenchyma and peritumoral microenvironment regions, a modeling strategy based on a multi-scale and multi-region dense connected convolutional neural network (MSMR-DenseCNNs) was proposed to predict histopathological grading using preoperative contrast enhanced computed tomography (CT) images. Multi-scale inputs were defined as three-scale enlargement of an original minimum bounding box in width and height by given pixels, which correspondingly contained more peritumoral analysis areas with the enlargement. Multi-region inputs were defined as three regions of interest (ROIs) including a squared ROI, a precisely delineated tumor ROI, and a peritumoral tissue ROI. The DenseCNN structure was designed to consist of a shallow feature extraction layer, dense block module, and transition and attention module. The proposed MSMR-DenseCNN was pretrained by the ImageNet dataset to capture basic graphic characteristics from the images and was retrained by the collected retrospective CT images. The predictive ability of the MSMR-DenseCNN models on triphasic images was compared with a conventional radiomics model, radiological model and clinical model.MSMR-DenseCNN applied to the delayed phase (DP) achieved the highest area under the curve (AUC) of 0.867 in the validation cohort for grading prediction, outperforming those on the arterial phase (AP) and portal venous phase (PVP). Fusion of the results on triphasic images did not increase the predictive ability, which underscored the role of DP for grading prediction. Compared with a single-scale and single-region network, the DP-phase based MSMR-DenseCNN model remarkably raised sensitivity from 67.4% to 75.5% with comparable specificity of 78.6%. MSMR-DenseCNN on DP defeated conventional radiomics, radiological and clinical models, where the AUCs were correspondingly 0.765, 0.695 and 0.612 in the validation cohort.The MSMR-DenseCNN modeling strategy increased the accuracy for preoperative prediction of grading in HCC, and enlightens similar radiological analysis pipelines in a variety of clinical scenarios in HCC management. This article is protected by copyright. All rights reserved.

Lymph node (LN) metastasis is significantly associated with worse prognosis for patients with intrahepatic cholangiocarcinoma (ICC). Improvement in preoperative assessment on LN metastasis helps in treatment decision-making. We aimed to investigate the role of radiomics-based method in predicting LN metastasis for patients with ICC.A total of 296 patients with ICC who underwent curative-intent hepatectomy and lymphadenectomy at two centers in China were analyzed. Radiomic features, including histogram- and wavelet-based features, shape and size features, and texture features were extracted from four-phase computerized tomography (CT) images. The clinical and conventional radiological variables which were independently associated with LN metastasis were also identified. A combined nomogram predicting LN metastasis was developed, and its performance was determined by discrimination, calibration, and stratification of long-term prognosis. The results were validated by the internal and external validation cohorts.Twenty-four radiomic features were selected into the nomogram. The established nomogram demonstrated good discrimination and calibration, with areas under the curve (AUCs) of 0.98 [95% confidence interval (CI) 0.96-0.99], 0.93 (0.88-0.98), and 0.89 (0.81-0.96) in the training and two validation cohorts, respectively. The 5-year overall survival (OS) and recurrence-free survival (RFS) rates of patients with high risk of LN metastasis as grouped by nomogram were poorer than those of patients with low risk in the training cohort (OS 28.8% versus 53.9%, p0.001; RFS 26.3% versus 44.2%, p = 0.001). Similar results were observed in the two validation cohorts.Radiomics-based method provided accurate prediction of LN metastasis and prognostic assessment for ICC patients, and might aid the preoperative surgical decision.

Microvascular invasion (MVI) is an important risk factor in hepatocellular carcinoma (HCC), but its diagnosis mandates postoperative histopathologic analysis. We aimed to develop and externally validate a predictive scoring system for MVI.From July 2015 to November 2020, consecutive patients underwent surgery for HCC with preoperative gadoxetate disodium (EOB)-enhanced MRI was retrospectively enrolled. All MR images were reviewed independently by two radiologists who were blinded to the outcomes. In the training centre, a radio-clinical MVI score was developed via logistic regression analysis against pathology. In the testing centre, areas under the receiver operating curve (AUCs) of the MVI score and other previous MVI schemes were compared. Overall survival (OS) and recurrence-free survival (RFS) were analysed by the Kaplan-Meier method with the log-rank test.A total of 417 patients were included, 195 (47%) with pathologically-confirmed MVI. The MVI score included: non-smooth tumour margin (odds ratio [OR] = 4.4), marked diffusion restriction (OR = 3.0), internal artery (OR = 3.0), hepatobiliary phase peritumoral hypointensity (OR = 2.5), tumour multifocality (OR = 1.6), and serum alpha-fetoprotein400 ng/mL (OR = 2.5). AUCs for the MVI score were 0.879 (training) and 0.800 (testing), significantly higher than those for other MVI schemes (testing AUCs: 0.648-0.684). Patients with model-predicted MVI had significantly shorter OS (median 61.0 months vs not reached, P .001) and RFS (median 13.0 months vs. 42.0 months, P .001) than those without.A preoperative MVI score integrating five EOB-MRI features and serum alpha-fetoprotein level could accurately predict MVI and postoperative survival in HCC. Therefore, this score may aid in individualized treatment decision making.

Accurate liver necroinflammatory activity diagnosis could guide the clinical decision-making in chronic hepatitis B (CHB) patients.This study aimed to build a non-invasive diagnostic model for liver necroinflammatory activity by incorporating deep learning features and clinico-biochemical characteristics in CHB patients. A total of 239 CHB patients who underwent liver biopsy were recruited and randomly divided into a training cohort (n = 179) and an independent validation cohort (n = 60). Bidirectional stepwise selection identified independent clinico-biochemical characteristics. Multivariate logistic regression analysis was used to establish the final combined model by incorporating clinico-biochemical and deep learning features. Predictive performance was evaluated by discrimination and clinical usefulness. Immunoglobulin M, platelets, laminin, type IV collagen, gamma-glutamyl transferase, alanine aminotransferase, aspartate transaminase, alkaline phosphatase, direct bilirubin, and total bilirubin were identified as independent factors. The combined model exhibited better performance than models based on clinico-biochemical characteristics alone, with an AUC of 0.942 (95% confidence interval [CI], 0.912-0.969) for necroinflammatory activity ≥G2 and 0.885 (95% CI, 0.829-0.934) for ≥G3 in the training cohort, and 0.938 (95% CI, 0.867-0.993) and 0.854 (95% CI, 0.764-0.934) in the validation cohort, respectively. The decision curve confirmed its clinical usefulness. The combined model provided an accurate non-invasive prediction of liver necroinflammatory activity, which might contribute to clinical decision-making in CHB patients.

The future of personalized diagnostics and treatment of cardiovascular diseases lies in the use of portable sensors. Portable sensors can acquire biomarker information in biological fluids such as sweat, an approach that mitigates the shortcomings of conventional hospital-centered healthcare. Low sensitivity, selectivity, and specificity remain bottlenecks for the widespread use of portable sensors. Herein, we demonstrate a portable sensor that simultaneously detects Na

Recently, image-based diagnostic technology has made encouraging and astonishing development. Modern medical care and imaging technology are increasingly inseparable. However, the current diagnosis pattern of Signal-to-Image-to-Knowledge inevitably leads to information distortion and noise introduction in the procedure of image reconstruction (Signal-to-Image). Artificial intelligence (AI) technologies that can mine knowledge from vast amounts of data offer opportunities to disrupt established workflows. In this prospective study, for the first time, we developed an AI-based Signal-to-Knowledge diagnostic scheme for lung nodule classification directly from the CT rawdata (the signal). We found that the rawdata achieved almost comparable performance with CT indicating that we can diagnose diseases without reconstructing images. Meanwhile, the introduction of rawdata could greatly promote the performance of CT, demonstrating that rawdata contains some diagnostic information that CT does not have. Our results break new ground and demonstrate the potential for direct Signal-to-Knowledge domain analysis.

Zona-free somatic cell transfer (SCT) and embryo aggregation increase throughput and efficiency of cloned embryo and offspring production, respectively, but both approaches have not been widely adopted. Cloning efficiency is further improved by cell cycle coordination between the interphase donor cell and metaphase-arrested recipient cytoplast. This commonly involves inclusion of caffeine and omission of calcium to maintain high mitotic cyclin-dependent kinase activity and low calcium levels, respectively, in the nonactivated cytoplast. The aim of our study was to integrate these various methodological improvements into a single work stream that increases sheep cloning success. We show that omitting calcium during zona-free SCT improved blastocyst development from 6% to 13%, while caffeine treatment reduced spontaneous oocyte activation from 17% to 8%. In a retrospective analysis, morula aggregation produced high morphological quality blastocysts with better in vivo survival to term than nonaggregated controls (15% vs. 9%), particularly after vitrification (14% vs. 0%). By combining cytoplast cell cycle control with zona-free embryo reconstruction and aggregation, this novel SCT protocol maximizes the benefits of vitrification by producing more cryoresilient blastocysts. The presented cloning methodology is relatively easy to operate and further increases throughput and efficiency of cloned embryo and offspring production. Integration of additional reprogramming steps or alternate donor cells is straightforward, providing a flexible workflow that can be adapted to changing experimental requirements.

Multiplying the germline would increase the number of offspring that can be produced from selected animals, accelerating genetic improvement for livestock breeding. This could be achieved by producing multiple chimaeric animals, each carrying a mix of donor and host germ cells in their gonads. However, such chimaeric germlines would produce offspring from both donor and host genotypes, limiting the rate of genetic improvement. To resolve this problem, we disrupted the RNA-binding protein DAZL and generated germ cell-deficient host animals. Using Cas9-mediated homology-directed repair (HDR), we introduced a DAZL loss-of-function mutation in male ovine fetal fibroblasts. Following manual single cell isolation, 4/48 (8.3%) of donor cell strains were homozygously HDR-edited. Sequence-validated strains were used as nuclear donors for somatic cell cloning to generate three lambs, which died at birth. All DAZL null male neonatal sheep lacked germ cells on histological sections and showed greatly reduced germ cell markers. Somatic cells within their testes were morphologically intact and expressed normal levels of lineage-specific markers, suggesting that the germ cell niche remained intact. This extends the DAZL mutant phenotype beyond mice into agriculturally relevant ruminants, providing a pathway for using absolute germline transmitters in rapid livestock improvement.

BACKGROUND Glypican 3 (GPC3) expression has proved to be a critical risk factor related to prognosis in hepatocellular carcinoma (HCC) patients. PURPOSE To investigate the performance of MRI-based radiomics signature in identifying GPC3-positive HCC. STUDY TYPE Retrospective. POPULATION An initial cohort of 293 patients with pathologically confirmed HCC was involved in this study, and patients were randomly divided into training (195) and validation (98) cohorts. FIELD STRENGTH/SEQUENCES Contrast-enhanced T1 -weight MRI was performed with a 1.5T scanner. ASSESSMENT A total of 853 radiomic features were extracted from the volume imaging. Univariate analysis and Fisher scoring were utilized for feature reduction. Subsequently, forward stepwise feature selection and radiomics signature building were performed based on a support vector machine (SVM). Incorporating independent risk factors, a combined nomogram was developed by multivariable logistic regression modeling. STATISTICAL TESTS The predictive performance of the nomogram was calculated using the area under the receive operating characteristic curve (AUC). Decision curve analysis (DCA) was applied to estimate the clinical usefulness. RESULTS The radiomics signature consisting of 10 selected features achieved good prediction efficacy (training cohort: AUC = 0.879, validation cohort: AUC = 0.871). Additionally, the combined nomogram integrating independent clinical risk factor α-fetoprotein (AFP) and radiomics signature showed improved calibration and prominent predictive performance with AUCs of 0.926 and 0.914 in the training and validation cohorts, respectively. DATA CONCLUSION The proposed MR-based radiomics signature is strongly related to GPC3-positive. The combined nomogram incorporating AFP and radiomics signature may provide an effective tool for noninvasive and individualized prediction of GPC3-positive in patients with HCC. J. MAGN. RESON. IMAGING 2020;52:1679-1687.

Medical image has been widely used in clinical application to aid decision making. It holds information related to tumor heterogeneity that cannot be easily recognized. In radiomics analysis, medical images are converted into mineable high-dimensional quantitative features by computer technology. Combining with related clinical problems, radiomics can provide help for prognosis and clinical decision making. The emergence of radiomics represents the transformation in the evaluation of medical imaging from a descriptive and qualitative discipline to a predictive and quantitative discipline. Hepatocellular carcinoma is one of the most common malignant tumors in China with high morbidity and mortality. Radiomics has been used in diagnosis, treatment selection and efficacy evaluation of hepatocellular carcinoma. Based on the basic principles and techniques of radiomics, this paper reviews the reaches in diagnosis, treatment selection and efficacy evaluation of hepatocellular carcinoma.

Correct reprogramming of epigenetic marks in the donor nucleus is a prerequisite for successful cloning by somatic cell transfer (SCT). In several mammalian species, repressive histone (H) lysine (K) trimethylation (me3) marks, in particular H3K9me3, form a major barrier to somatic cell reprogramming into pluripotency and totipotency. We engineered bovine embryonic fibroblasts (BEFs) for the doxycycline-inducible expression of a biologically active, truncated form of murine Kdm4b, a demethylase that removes H3K9me3 and H3K36me3 marks. Upon inducing Kdm4b, H3K9me3 and H3K36me3 levels were reduced about 3-fold and 5-fold, respectively, compared with noninduced controls. Donor cell quiescence has been previously associated with reduced somatic trimethylation levels and increased cloning efficiency in cattle. Simultaneously inducing Kdm4b expression (via doxycycline) and quiescence (via serum starvation) further reduced global H3K9me3 and H3K36me3 levels by a total of 18-fold and 35-fold, respectively, compared with noninduced, nonstarved control fibroblasts. Following SCT, Kdm4b-BEFs reprogrammed significantly better into cloned blastocysts than noninduced donor cells. However, detrimethylated donors and sustained Kdm4b-induction during embryo culture did not increase the rates of postblastocyst development from implantation to survival into adulthood. In summary, overexpressing Kdm4b in donor cells only improved their reprogramming into early preimplantation stages, highlighting the need for alternative experimental approaches to reliably improve somatic cloning efficiency in cattle.

Background Biopsy Gleason score (GS) is crucial for prostate cancer (PCa) treatment decision-making. Upgrading in GS from biopsy to radical prostatectomy (RP) puts a proportion of patients at risk of undertreatment. Purpose To develop and validate a radiomics model based on multiparametric magnetic resonance imaging (mp-MRI) to predict PCa upgrading. Study type Retrospective, radiomics. Population A total of 166 RP-confirmed PCa patients (training cohort, n = 116; validation cohort, n = 50) were included. Field strength/sequence 3.0T/T2 -weighted (T2 W), apparent diffusion coefficient (ADC), and dynamic contrast enhancement (DCE) sequences. Assessment PI-RADSv2 score for each tumor was recorded. Radiomic features were extracted from T2 W, ADC, and DCE sequences and Mutual Information Maximization criterion was used to identify the optimal features on each sequence. Multivariate logistic regression analysis was used to develop predictive models and a radiomics nomogram and their performance was evaluated. Statistical tests Student's t or chi-square were used to assess the differences in clinicopathologic data between the training and validation cohorts. Receiver operating characteristic (ROC) curve analysis was performed and the area under the curve (AUC) was calculated. Results In PI-RADSv2 assessment, 67 lesions scored 5, 70 lesions scored 4, and 29 lesions scored 3. For each sequence, 4404 features were extracted and the top 20 best features were selected. The radiomics model incorporating signatures from the three sequences achieved better performance than any single sequence (AUC: radiomics model 0.868, T2 W 0.700, ADC 0.759, DCE 0.726). The combined mode incorporating radiomics signature, clinical stage, and time from biopsy to RP outperformed the clinical model and radiomics model (AUC: combined model 0.910, clinical model 0.646, radiomics model 0.868). The nomogram showed good performance (AUC 0.910) and calibration (P-values: training cohort 0.624, validation cohort 0.294). Data conclusion Radiomics based on mp-MRI has potential to predict upgrading of PCa from biopsy to RP. Level of evidence 3 TECHNICAL EFFICACY: Stage 5 J. Magn. Reson. Imaging 2020;52:1239-1248.

Background High-producing Holstein Friesian dairy cattle have a characteristic black and white coat, often with large proportions of black. Compared to a light coat color, black absorbs more solar radiation which is a contributing factor to heat stress in cattle. To better adapt dairy cattle to rapidly warming climates, we aimed to lighten their coat color by genome editing. Results Using gRNA/Cas9-mediated editing, we introduced a three bp deletion in the pre-melanosomal protein 17 gene (PMEL) proposed as causative variant for the semi-dominant color dilution phenotype observed in Galloway and Highland cattle. Calves generated from cells with homozygous edits revealed a strong color dilution effect. Instead of the characteristic black and white markings of control calves generated from unedited cells, the edited calves displayed a novel grey and white coat pattern. Conclusion This, for the first time, verified the causative nature of the PMEL mutation for diluting the black coat color in cattle. Although only one of the calves was healthy at birth and later succumbed to a naval infection, the study showed the feasibility of generating such edited animals with the possibility to dissect the effects of the introgressed edit and other interfering allelic variants that might exist in individual cattle and accurately determine the impact of only the three bp change.

Background Photosynthesis in the green leafless blade tissues or organs of plants has been studied in some plants, but the photosynthetic characteristics of stems and petioles are poorly understood. Cucurbitaceous plants are climbing plants that have substantial stem and petiole biomass. Understanding the photosynthetic contribution of cucumber stems and petioles to their growth and the underlying molecular mechanisms are important for the regulating of growth in cucumber production. Results In this study, the photosynthetic capacity of cucumber stems and petioles were determined by 14CO2 uptake. The total carbon fixed by the stems and petioles was approximately 4% of that fixed by one leaf blade in the cucumber seedling stage, while the proportion of the carbon accumulated in the stems and petioles that redistributed to sink organs (roots and shoot apexes) obviously increased under leafless conditions. The photosynthetic properties of cucumber stems and petioles were studied using a combination of electron microscopy and isotope tracers to compare these properties of stems and petioles with those of leaf blade using two genotypes of cucumber (dark green and light green). Compared with those of the leaf blades, the chlorophyll contents of the cucumber stems and petioles were lower, and the stems and petioles had lower chloroplast numbers and lower stoma numbers but higher thylakoid grana lamella numbers and larger stoma sizes. The Chl a/b ratios were also decreased in the petioles and stems compared with those in the leaf blades. The total photosynthetic rates of the stems and petioles were equivalent to 6 ~ 8% of that of one leaf blade, but the respiration rates were similar in all the three organs, with an almost net 0 photosynthetic rate in the stems and petioles. Transcriptome analysis showed that compared with the leaf blades, the stems and petioles has significantly different gene expression levels in photosynthesis, porphyrin and chlorophyll metabolism; photosynthetic antenna proteins; and carbon fixation. PEPC enzyme activities were higher in the stems and petioles than in the leaf blades, suggesting that the photosynthetic and respiratory mechanisms in stems and petioles are different from those in leaf blade, and these results are consistent with the gene expression data. Conclusions In this study, we confirmed the photosynthetic contribution to the growth of cucumber stems and petioles, and showed their similar photosynthetic patterns in the terms of anatomy, molecular biology and physiology, which were different from those of cucumber leaf blades.

We aimed to develop a radiomics-based model derived from gadoxetic acid–enhanced MR images to preoperatively identify cytokeratin (CK) 19 status of hepatocellular carcinoma (HCC). A cohort of 227 patients with single HCC was classified into a training set (n = 159) and a time-independent validated set (n = 68). A total of 647 radiomic features were extracted from multi-sequence MR images. The least absolute shrinkage and selection operator regression and decision tree methods were utilized for feature selection and radiomics signature construction. A multivariable logistic regression model incorporating clinico-radiological features and the fusion radiomics signature was built for prediction of CK19 status by evaluating area under curve (AUC). In the whole cohort, 57 patients were CK19 positive and 170 patients were CK19 negative. By combining 11 and 6 radiomic features extracted in arterial phase and hepatobiliary phase images, respectively, a fusion radiomics signature achieved AUCs of 0.951 and 0.822 in training and validation datasets. The final combined model integrated a-fetoprotein levels, arterial rim enhancement pattern, irregular tumor margin, and the fusion radiomics signature, with a sensitivity of 0.818 and specificity of 0.974 in the training cohort and that of 0.769 and 0.818 in the validated cohort. The nomogram based on the combined model showed satisfactory prediction performance in training (C-index 0.959) and validation (C-index 0.846) dataset. The combined model based on a fusion radiomics signature derived from arterial and hepatobiliary phase images of gadoxetic acid–enhanced MRI can be a reliable biomarker for CK19 status of HCC. • Arterial rim enhancement pattern and irregular tumor margin on hepatobiliary phase on gadoxetic acid–enhanced MRI can be useful for evaluating CK19 status of HCC. • A radiomics-based model performed better than the clinico-radiological model both in training and validation datasets for predicting CK19 status of HCC. • The nomogram based on the fusion radiomics signature can be easily used for CK19 stratification of HCC.

To predict histopathologic growth patterns (HGPs) in colorectal liver metastases (CRLMs) with a noninvasive radiomics model. Patients with chemotherapy-naive CRLMs who underwent abdominal contrast-enhanced multidetector CT (MDCT) followed by partial hepatectomy between January 2007 and January 2019 from two institutions were included in this retrospective study. Hematoxylin- and eosin-stained histopathologic sections of CRLMs were reviewed, with HGPs defined according to international consensus. Lesions were divided into training and validation datasets based on patients’ sources. Radiomic features were extracted from pre- and post-contrast (arterial and portal venous) phase MDCT images, with review focusing on the segmented tumor–liver interface zones of CRLMs. Minimum redundancy maximum relevance and decision tree methods were used for radiomics modeling. Multivariable logistic regression analyses and ROC curves were used to assess the predictive performance of these models in predicting HGP types. A total of 126 CRLMs with histopathologic-demonstrated desmoplastic (n = 68) or replacement (n = 58) HGPs were assessed. The radiomics signature consisted of 20 features of each phase selected. The 3 phases fused radiomics signature demonstrated the best predictive performance in distinguishing between replacement and desmoplastic HGPs (AUCs of 0.926 and 0.939 in the training and external validation cohorts, respectively). The clinical-radiomics combined model showed good discrimination (C-indices of 0.941 and 0.833 in the training and external validation cohorts, respectively). A radiomics model derived from MDCT images may effectively predict the HGP of CRLMs, thus providing a basis for prognostic stratification and therapeutic decision-making.

To develop and validate a radiomics-based nomogram for preoperatively predicting grade 1 and grade 2/3 tumors in patients with pancreatic neuroendocrine tumors (PNETs). One hundred thirty-eight patients derived from two institutions with pathologically confirmed PNETs (104 in the training cohort and 34 in the validation cohort) were included in this retrospective study. A total of 853 radiomic features were extracted from arterial and portal venous phase CT images respectively. Minimum redundancy maximum relevance and random forest methods were adopted for the significant radiomic feature selection and radiomic signature construction. A fusion radiomic signature was generated by combining both the single-phase signatures. The nomogram based on a comprehensive model incorporating the clinical risk factors and the fusion radiomic signature was established, and decision curve analysis was applied for clinical use. The fusion radiomic signature has significant association with histologic grade (p

To develop and validate a radiomics nomogram for preoperative prediction of microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC). The study included 157 patients with histologically confirmed HCC with or without MVI, and 110 patients were allocated to the training dataset and 47 to the validation dataset. Baseline clinical factor (CF) data were collected from our medical records, and radiomics features were extracted from the artery phase (AP), portal venous phase (PVP) and delay phase (DP) of preoperatively acquired CT in all patients. Radiomics analysis included tumour segmentation, feature extraction, model construction and model evaluation. A final nomogram for predicting MVI of HCC was established. Nomogram performance was assessed via both calibration and discrimination statistics. Five AP features, seven PVP features and nine DP features were effective for MVI prediction in HCC radiomics signatures. PVP radiomics signatures exhibited better performance than AP and DP radiomics signatures in the validation datasets, with the AUC 0.793. In the clinical model, age, maximum tumour diameter, alpha-fetoprotein and hepatitis B antigen were effective predictors. The final nomogram integrated the PVP radiomics signature and four CFs. Good calibration was achieved for the nomogram in both the training and validated datasets, with respective C-indexes of 0.827 and 0.820. Decision curve analysis suggested that the proposed nomogram was clinically useful, with a corresponding net benefit of 0.357. The above-described radiomics nomogram can preoperatively predict MVI in patients with HCC and may constitute a usefully clinical tool to guide subsequent personalised treatment. • No previously reported study has utilised radiomics nomograms to preoperatively predict the MVI of HCC using 3D contrast-enhanced CT imaging. • The combined radiomics clinical factor (CF) nomogram for predicting MVI achieved superior performance than either the radiomics signature or the CF nomogram alone. • Nomograms combing PVP radiomics and CF may be useful as an imaging marker for predicting MVI of HCC preoperatively and could guide personalised treatment.

The hepatic venous pressure gradient (HVPG) is the gold standard for cirrhotic portal hypertension (PHT), but it is invasive and specialized. Alternative non-invasive techniques are needed to assess the hepatic venous pressure gradient (HVPG). Here, we develop an auto-machine-learning CT radiomics HVPG quantitative model (aHVPG), and then we validate the model in internal and external test datasets by the area under the receiver operating characteristic curves (AUCs) for HVPG stages (≥10, ≥12, ≥16, and ≥20 mm Hg) and compare the model with imaging- and serum-based tools. The final aHVPG model achieves AUCs over 0.80 and outperforms other non-invasive tools for assessing HVPG. The model shows performance improvement in identifying the severity of PHT, which may help non-invasive HVPG primary prophylaxis when transjugular HVPG measurements are not available.

Macrovascular invasion (MaVI) occurs in nearly half of hepatocellular carcinoma (HCC) patients at diagnosis or during follow-up, which causes severe disease deterioration, and limits the possibility of surgical approaches. This study aimed to investigate whether computed tomography (CT)-based radiomics analysis could help predict development of MaVI in HCC.A cohort of 226 patients diagnosed with HCC was enrolled from 5 hospitals with complete MaVI and prognosis follow-ups. CT-based radiomics signature was built via multi-strategy machine learning methods. Afterwards, MaVI-related clinical factors and radiomics signature were integrated to construct the final prediction model (CRIM, clinical-radiomics integrated model) via random forest modeling. Cox-regression analysis was used to select independent risk factors to predict the time of MaVI development. Kaplan-Meier analysis was conducted to stratify patients according to the time of MaVI development, progression-free survival (PFS), and overall survival (OS) based on the selected risk factors.The radiomics signature showed significant improvement for MaVI prediction compared with conventional clinical/radiological predictors (P0.001). CRIM could predict MaVI with satisfactory areas under the curve (AUC) of 0.986 and 0.979 in the training (n = 154) and external validation (n = 72) datasets, respectively. CRIM presented with excellent generalization with AUC of 0.956, 1.000, and 1.000 in each external cohort that accepted disparate CT scanning protocol/manufactory. Peel9_fos_InterquartileRange [hazard ratio (HR) = 1.98; P0.001] was selected as the independent risk factor. The cox-regression model successfully stratified patients into the high-risk and low-risk groups regarding the time of MaVI development (P0.001), PFS (P0.001) and OS (P = 0.002).The CT-based quantitative radiomics analysis could enable high accuracy prediction of subsequent MaVI development in HCC with prognostic implications.

Low-dose CT has been a key diagnostic imaging modality to reduce the potential risk of radiation overdose to patient health. Despite recent advances, CNN-based approaches typically apply filters in a spatially invariant way and adopt similar pixel-level losses, which treat all regions of the CT image equally and can be inefficient when fine-grained structures coexist with non-uniformly distributed noises. To address this issue, we propose a Structure-preserving Kernel Prediction Network (StructKPN) that combines the kernel prediction network with a structure-aware loss function that utilizes the pixel gradient statistics and guides the model towards spatially-variant filters that enhance noise removal, prevent over-smoothing and preserve detailed structures for different regions in CT imaging. Extensive experiments demonstrated that our approach achieved superior performance on both synthetic and non-synthetic datasets, and better preserves structures that are highly desired in clinical screening and low-dose protocol optimization., ICIP2021

Microvascular invasion (MVI) is a critical risk factor for postoperative recurrence of hepatocellular carcinoma (HCC). Preknowledge of MVI would assist tailored surgery planning in HCC management. In this multicenter study, we aimed to explore the validity of deep learning (DL) in MVI prediction using two imaging modalities—contrast-enhanced computed tomography (CE-CT) and gadoxetic acid-enhanced magnetic resonance imaging (EOB-MRI). A total of 750 HCCs were enrolled from five Chinese tertiary hospitals. Retrospective CE-CT (n = 306, collected between March, 2013 and July, 2019) and EOB-MRI (n = 329, collected between March, 2012 and March, 2019) data were used to train two DL models, respectively. Prospective external validation (n = 115, collected between July, 2015 and February, 2018) was performed to assess the developed models. Furthermore, DL-based attention maps were utilized to visualize high-risk MVI regions. Our findings revealed that the EOB-MRI-based DL model achieved superior prediction outcome to the CE-CT-based DL model (area under receiver operating characteristics curve (AUC): 0.812 vs. 0.736, p = 0.038, sensitivity: 70.4% vs. 57.4%, p = 0.015, specificity: 80.3% vs. 86.9%, p = 0.052). DL attention maps could visualize peritumoral high-risk areas with genuine histopathologic confirmation. Both DL models could stratify high and low-risk groups regarding progression free survival and overall survival (p &lt, 0.05). Thus, DL can be an efficient tool for MVI prediction, and EOB-MRI was proven to be the modality with advantage for MVI assessment than CE-CT.

Zona-free somatic cell transfer (SCT) and embryo aggregation increase throughput and efficiency of cloned embryo and offspring production, respectively, but both approaches have not been widely adopted. Cloning efficiency is further improved by cell cycle coordination between the interphase donor cell and metaphase-arrested recipient cytoplast. This commonly involves inclusion of caffeine and omission of calcium to maintain high mitotic cyclin-dependent kinase activity and low calcium levels, respectively, in the nonactivated cytoplast. The aim of our study was to integrate these various methodological improvements into a single work stream that increases sheep cloning success. We show that omitting calcium during zona-free SCT improved blastocyst development from 6% to 13%, while caffeine treatment reduced spontaneous oocyte activation from 17% to 8%. In a retrospective analysis, morula aggregation produced high morphological quality blastocysts with better