An Integrated Sweat Sensor for Synchronous Detection of Multiple Atherosclerosis Biomarkers.

Atherosclerosis conditions are often assessed in the clinic by measuring blood viscosity, blood flow, and blood lesion levels. In alignment with precision medicine, it is essential to develop convenient and noninvasive approaches for atherosclerosis diagnostics. Herein, an integrated electrochemical sensor was successfully demonstrated for simultaneously detecting cholesterol, transferrin, and K+ in sweat, all biomarker indicators of atherosclerosis. The sensing substrate was based on carbon quantum dots integrated within multiwalled carbon nanotubes, creating a hybrid framework with low electron transfer resistance and highly efficient electron transfer rate, yielding a highly electrochemical active platform for ultrasensitive detection of trace sweat biomarkers. To ensure specificity to corresponding targets, the sensing mechanisms were based on molecular recognition reactions of cholesterol and ß-cyclodextrin, transferrin and molecular cavities, and K+ and ion-selective permeation membrane. Moreover, the integrated nonenzymatic sensor exhibited excellent long-term stability. Furthermore, the practical utility of the sensor was successfully demonstrated by the simultaneous detection of three atherosclerosis biomarkers in sweat from volunteers who underwent predesigned daily activities. The sensor shows promise for convenient indexing of atherosclerosis conditions in a noninvasive way.

Mutation of CsARC6 affects fruit color and increases fruit nutrition in cucumber.

KEY MESSAGE: A mutation of CsARC6 not only causes white fruit color in cucumber, but also affects plant growth and fruit quality. Fruit color of cucumber is a very important agronomic trait, but most of the genes affecting cucumber white fruit color are still unknow, and no further studies were reported on the effect of cucumber fruit quality caused by white fruit color genes. Here, we obtained a white fruit mutant em41 in cucumber by EMS mutagenesis. The mutant gene was mapped to a 548 kb region of chromosome 2. Through mutation site analysis, it was found to be a null allele of CsARC6 (CsaV3_2G029290). The Csarc6 mutant has a typical phenotype of arc6 mutant that mesophyll cells contained only one or two giant chloroplasts. ARC6 protein was not detected in em41, and the level of FtsZ1 and FtsZ2 was also reduced. In addition, FtsZ2 could not form FtsZ ring-like structures in em41. Although these are typical arc6 mutant phenotypes, some special phenotypes occur in Csarc6 mutant, such as dwarfness with shortened internodes, enlarged fruit epidermal cells, decreased carotenoid contents, smaller fruits, and increased fruit nutrient contents. This study discovered a new gene, CsARC6, which not only controls the white fruit color, but also affects plant growth and fruit quality in cucumber.

Production of light-coloured, low heat-absorbing Holstein Friesian cattle by precise embryo-mediated genome editing.

CONTEXT: Genome editing enables the introduction of beneficial sequence variants into the genomes of animals with high genetic merit in a single generation. This can be achieved by introducing variants into primary cells followed by producing a live animal from these cells by somatic cell nuclear transfer cloning. The latter step is associated with low efficiencies and developmental problems due to incorrect reprogramming of the donor cells, causing animal welfare concerns. Direct editing of fertilised one-cell embryos could circumvent this issue and might better integrate with genetic improvement strategies implemented by the industry. METHODS: In vitro fertilised zygotes were injected with TALEN editors and repair template to introduce a known coat colour dilution mutation in the PMEL gene. Embryo biopsies of injected embryos were screened by polymerase chain reaction and sequencing for intended biallelic edits before transferring verified embryos into recipients for development to term. Calves were genotyped and their coats scanned with visible and hyperspectral cameras to assess thermal energy absorption. KEY RESULTS: Multiple non-mosaic calves with precision edited genotypes were produced, including calves from high genetic merit parents. Compared to controls, the edited calves showed a strong coat colour dilution which was associated with lower thermal energy absorbance. CONCLUSIONS: Although biopsy screening was not absolutely accurate, non-mosaic, precisely edited calves can be readily produced by embryo-mediated editing. The lighter coat colouring caused by the PMEL mutation can lower radiative heat gain which might help to reduce heat stress. IMPLICATIONS: The study validates putative causative sequence variants to rapidly adapt grazing cattle to changing environmental conditions.

A Portable Sweat Sensor Based on Carbon Quantum Dots for Multiplex Detection of Cardiovascular Health Biomarkers.

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+, ascorbic acid, and human neuropeptide Y in sweat, all useful biomarkers to index cardiovascular health. The portable sensor comprises a six-electrode system containing three working electrodes, two reference electrodes, and one counter electrode. The working electrodes were prepared by depositing sensing components on carbon quantum dot (CQD) electrodes. The sensing mechanisms were based on selective ion recognition, enzyme catalytic reaction, and immune response, which guarantees specificity to corresponding targets. The CQDs offer massive reactive sites and effectively reduce the interfacial impedance during the sensing reaction, thereby enhancing the three biomarkers' detection sensitivity. As evidence of portable sensor capability, we demonstrate herein its effective simultaneous detection of the three biomarkers in a real sweat from healthy volunteers during routine activities including exercise, extra ascorbic acid ingestion, and extra Na+ ingestion. As such, the sensor shows promise for real-time noninvasive personalized medical diagnostics and metabolic wellness management.

Radiomics-Based Preoperative Prediction of Lymph Node Metastasis in Intrahepatic Cholangiocarcinoma Using Contrast-Enhanced Computed Tomography.

BACKGROUND: 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. METHODS: 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. RESULTS: 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%, p < 0.001; RFS 26.3% versus 44.2%, p = 0.001). Similar results were observed in the two validation cohorts. CONCLUSIONS: Radiomics-based method provided accurate prediction of LN metastasis and prognostic assessment for ICC patients, and might aid the preoperative surgical decision.

Predicting microvascular invasion in hepatocellular carcinoma: A dual-institution study on gadoxetate disodium-enhanced MRI.

BACKGROUND & AIMS: 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. METHODS: 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. RESULTS: 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-fetoprotein >400 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. CONCLUSIONS: 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.

CT-based radiomics to predict development of macrovascular invasion in hepatocellular carcinoma: A multicenter study.

BACKGROUND: 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. METHODS: 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. RESULTS: The radiomics signature showed significant improvement for MaVI prediction compared with conventional clinical/radiological predictors (P < 0.001). 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; P < 0.001] 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 (P < 0.001), PFS (P < 0.001) and OS (P = 0.002). CONCLUSIONS: The CT-based quantitative radiomics analysis could enable high accuracy prediction of subsequent MaVI development in HCC with prognostic implications.

Photosynthetic contribution and characteristics of cucumber stems and petioles.

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.

Testes of DAZL null neonatal sheep lack prospermatogonia but maintain normal somatic cell morphology and marker expression.

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.

Multi-Omics and Its Clinical Application in Hepatocellular Carcinoma: Current Progress and Future Opportunities.

Hepatocellular carcinoma (HCC) is the sixth most common malignancy and the fourth leading cause of cancer related death worldwide. China covers over half of cases, leading HCC to be a vital threaten to public health. Despite advances in diagnosis and treatments, high recurrence rate remains a major obstacle in HCC management. Multi-omics currently facilitates surveillance, precise diagnosis, and personalized treatment decision making in clinical setting. Non-invasive radiomics utilizes preoperative radiological imaging to reflect subtle pixel-level pattern changes that correlate to specific clinical outcomes. Radiomics has been widely used in histopathological diagnosis prediction, treatment response evaluation, and prognosis prediction. High-throughput sequencing and gene expression profiling enabled genomics and proteomics to identify distinct transcriptomic subclasses and recurrent genetic alterations in HCC, which would reveal the complex multistep process of the pathophysiology. The accumulation of big medical data and the development of artificial intelligence techniques are providing new insights for our better understanding of the mechanism of HCC via multi-omics, and show potential to convert surgical/intervention treatment into an antitumorigenic one, which would greatly advance precision medicine in HCC management.

Targeted histone demethylation improves somatic cell reprogramming into cloned blastocysts but not postimplantation bovine concepti†.

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.

MRI-Based Radiomics Signature: A Potential Biomarker for Identifying Glypican 3-Positive Hepatocellular Carcinoma.

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.

Multiparametric MRI-Based Radiomics for Prostate Cancer Screening With PSA in 4-10 ng/mL to Reduce Unnecessary Biopsies.

BACKGROUND: Whether men with a prostate-specific antigen (PSA) level of 4-10 ng/mL should be recommended for a biopsy is clinically challenging. PURPOSE: To develop and validate a radiomics model based on multiparametric MRI (mp-MRI) in patients with PSA levels of 4-10 ng/mL to predict prostate cancer (PCa) preoperatively and reduce unnecessary biopsies. STUDY TYPE: Retrospective. SUBJECTS: In all, 199 patients with PSA levels of 4-10 ng/mL. FIELD STRENGTH/SEQUENCE: 3T, T2 -weighted, diffusion-weighted, and dynamic contrast-enhanced MRI. ASSESSMENT: Lesion regions of interest (ROIs) from T2 -weighted, diffusion-weighted, and dynamic contrast-enhanced MRI were annotated by two radiologists. A total of 2104 radiomic features were extracted from the ROI of each patient. A random forest classifier was used to build the radiomics model for PCa in the primary cohort. A combined model was constructed using multivariate logistic regression by incorporating the radiomics signature and clinical-radiological risk factors. STATISTICAL TESTS: For continuous variables, variance equality was assessed by Levene's test and Student's t-test, and Welch's t-test was used to assess between-group differences. For categorical variables, Pearson's chi-square test, Fisher's exact test, or the approximate chi-square test was used to assess between-group differences. P < 0.05 was considered statistically significant. RESULTS: The combined model incorporating the multi-imaging fusion model, age, PSA density (PSAD), and the PI-RADS v2 score yielded area under the curve (AUC) values of 0.956 and 0.933 on the primary (n = 133) and validation (n = 66) cohorts, respectively. Compared with the clinical-radiological model, the combined model performed better on both the primary and validation cohorts (P < 0.05). Furthermore, the use of the combined model to predict PCa could identify more negative PCa patients than the use of the clinical-radiological model by 18.4%. DATA CONCLUSION: The combined model was developed and validated to provide potential preoperative prediction of PCa in men with PSA levels of 4-10 ng/mL and might aid in treatment decision-making and reduce unnecessary biopsies. LEVEL OF EVIDENCE: 3 Technical Efficacy Stage: 3 J. Magn. Reson. Imaging 2020;51:1890-1899.

Radiomics Based on MRI as a Biomarker to Guide Therapy by Predicting Upgrading of Prostate Cancer From Biopsy to Radical Prostatectomy.

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.

Radiomics in liver diseases: Current progress and future opportunities.

Liver diseases, a wide spectrum of pathologies from inflammation to neoplasm, have become an increasingly significant health problem worldwide. Noninvasive imaging plays a critical role in the clinical workflow of liver diseases, but conventional imaging assessment may provide limited information. Accurate detection, characterization and monitoring remain challenging. With progress in quantitative imaging analysis techniques, radiomics emerged as an efficient tool that shows promise to aid in personalized diagnosis and treatment decision-making. Radiomics could reflect the heterogeneity of liver lesions via extracting high-throughput and high-dimensional features from multi-modality imaging. Machine learning algorithms are then used to construct clinical target-oriented imaging biomarkers to assist disease management. Here, we review the methodological process in liver disease radiomics studies in a stepwise fashion from data acquisition and curation, region of interest segmentation, liver-specific feature extraction, to task-oriented modelling. Furthermore, the applications of radiomics in liver diseases are outlined in aspects of diagnosis and staging, evaluation of liver tumour biological behaviours, and prognosis according to different disease type. Finally, we discuss the current limitations of radiomics in liver disease studies and explore its future opportunities.

A radiomics-based biomarker for cytokeratin 19 status of hepatocellular carcinoma with gadoxetic acid-enhanced MRI.

OBJECTIVES: 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). METHODS: 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). RESULTS: 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. CONCLUSIONS: 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. KEY POINTS: • 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.

Prediction of Histopathologic Growth Patterns of Colorectal Liver Metastases with a Noninvasive Imaging Method.

OBJECTIVES: To predict histopathologic growth patterns (HGPs) in colorectal liver metastases (CRLMs) with a noninvasive radiomics model. METHODS: 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. RESULTS: 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). CONCLUSIONS: 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.

Preoperative radiomics nomogram for microvascular invasion prediction in hepatocellular carcinoma using contrast-enhanced CT.

OBJECTIVES: To develop and validate a radiomics nomogram for preoperative prediction of microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC). METHODS: 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. RESULTS: 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. CONCLUSIONS: 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. KEY POINTS: • 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.

CT radiomics may predict the grade of pancreatic neuroendocrine tumors: a multicenter study.

OBJECTIVE: 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). METHODS: 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. RESULTS: The fusion radiomic signature has significant association with histologic grade (p < 0.001). The nomogram integrating independent clinical risk factor tumor margin and fusion radiomic signature showed strong discrimination with an area under the curve (AUC) of 0.974 (95% CI 0.950-0.998) in the training cohort and 0.902 (95% CI 0.798-1.000) in the validation cohort with good calibration. Decision curve analysis verified the clinical usefulness of the predictive nomogram. CONCLUSION: We proposed a comprehensive nomogram consisting of tumor margin and fusion radiomic signature as a powerful tool to predict grade 1 and grade 2/3 PNET preoperatively and assist the clinical decision-making for PNET patients. KEY POINTS: • Radiomic signature has strong discriminatory ability for the histologic grade of PNETs. • Arterial and portal venous phase CT imaging are complementary for the prediction of PNET grading. • The comprehensive nomogram outperformed clinical factors in assisting therapy strategy in PNET patients.

A radiomics nomogram may improve the prediction of IDH genotype for astrocytoma before surgery.

OBJECTIVES: To develop and validate a radiomics nomogram to preoperative prediction of isocitrate dehydrogenase (IDH) genotype for astrocytomas, which might contribute to the pretreatment decision-making and prognosis evaluating. METHODS: One hundred five astrocytomas (Grades II-IV) with contrast-enhanced T1-weighted imaging (CE-T1WI), T2 fluid-attenuated inversion recovery (T2FLAIR), and apparent diffusion coefficient (ADC) map were enrolled in this study (training cohort: n = 74; validation cohort: n = 31). IDH1/2 genotypes were determined using Sanger sequencing. A total of 3882 radiomics features were extracted. Support vector machine algorithm was used to build the radiomics signature on the training cohort. Incorporating radiomics signature and clinico-radiological risk factors, the radiomics nomogram was developed. Receiver operating characteristic (ROC) curve and area under the curve (AUC) were used to assess these models. Kaplan-Meier survival analysis and log rank test were performed to assess the prognostic value of the radiomics nomogram. RESULTS: The radiomics signature was built by six selected radiomics features and yielded AUC values of 0.901 and 0.888 in the training and validation cohorts. The radiomics nomogram based on the radiomics signature and age performed better than the clinico-radiological model (training cohort, AUC = 0.913 and 0.817; validation cohort, AUC = 0.900 and 0.804). Additionally, the survival analysis showed that prognostic values of the radiomics nomogram and IDH genotype were similar (log rank test, p < 0.001; C-index = 0.762 and 0.687; z-score test, p = 0.062). CONCLUSIONS: The radiomics nomogram might be a useful supporting tool for the preoperative prediction of IDH genotype for astrocytoma, which could aid pretreatment decision-making. KEY POINTS: • The radiomics signature based on multiparametric and multiregional MRI images could predict IDH genotype of Grades II-IV astrocytomas. • The radiomics nomogram performed better than the clinico-radiological model, and it might be an easy-to-use supporting tool for IDH genotype prediction. • The prognostic value of the radiomics nomogram was similar with that of the IDH genotype, which might contribute to prognosis evaluating.