Ancestral whole-genome duplication in the marine chelicerate horseshoe crabs

Whole-genome duplication (WGD) results in new genomic resources that can be exploited by evolution for rewiring genetic regulatory networks in organisms. In metazoans, WGD occurred before the last common ancestor of vertebrates, and has been postulated as a major evolutionary force that contributed to their speciation and diversification of morphological structures. Here, we have sequenced genomes from three of the four extant species of horseshoe crabs-Carcinoscorpius rotundicauda, Limulus polyphemus and Tachypleus tridentatus. Phylogenetic and sequence analyses of their Hox and other homeobox genes, which encode crucial transcription factors and have been used as indicators of WGD in animals, strongly suggests that WGD happened before the last common ancestor of these marine chelicerates >135 million years ago. Signatures of subfunctionalisation of paralogues of Hox genes are revealed in the appendages of two species of horseshoe crabs. Further, residual homeobox pseudogenes are observed in the three lineages. The existence of WGD in the horseshoe crabs, noted for relative morphological stasis over geological time, suggests that genomic diversity need not always be reflected phenotypically, in contrast to the suggested situation in vertebrates. This study provides evidence of ancient WGD in the ecdysozoan lineage, and reveals new opportunities for studying genomic and regulatory evolution after WGD in the Metazoa.

An ultrasound-based radiomics model to distinguish between sclerosing adenosis and invasive ductal carcinoma

Objectives

We aimed to develop an ultrasound-based radiomics model to distinguish between sclerosing adenosis (SA) and invasive ductal carcinoma (IDC) to avoid misdiagnosis and unnecessary biopsies.

Methods

From January 2020 to March 2022, 345 cases of SA or IDC that were pathologically confirmed were included in the study. All participants underwent pre-surgical ultrasound (US), from which clinical information and ultrasound images were collected. The patients from the study population were randomly divided into a training cohort (n = 208) and a validation cohort (n = 137). The US images were imported into MaZda software (Version 4.2.6.0) to delineate the region of interest (ROI) and extract features. Intragroup correlation coefficient (ICC) was used to evaluate the consistency of the extracted features. The least absolute shrinkage and selection operator (LASSO) logistic regression and cross-validation were performed to obtain the radiomics score of the features. Based on univariate and multivariate logistic regression analyses, a model was developed. 56 cases from April 2022 to December 2022 were included for independent validation of the model. The diagnostic performance of the model and the radiomics scores were evaluated by performing the receiver operating characteristic (ROC) analysis. The calibration curve and decision curve analysis (DCA) were used for calibration and evaluation. Leave-One-Out Cross-Validation (LOOCV) was used for the stability of the model.

Results

Three predictors were selected to develop the model, including radiomics score, palpable mass and BI-RADS. In the training cohort, validation cohort and independent validation cohort, AUC of the model and radiomics score were 0.978 and 0.907, 0.946 and 0.886, 0.951 and 0.779, respectively. The model showed a statistically significant difference compared with the radiomics score (p<0.05). The Kappa value of the model was 0.79 based on LOOCV. The Brier score, calibration curve, and DCA showed the model had a good calibration and clinical usefulness.

Conclusions

The model based on radiomics, ultrasonic features, and clinical manifestations can be used to distinguish SA from IDC, which showed good stability and diagnostic performance. The model can be considered a potential candidate diagnostic tool for breast lesions and can contribute to effective clinical diagnosis.

An Accurate Model for Microvascular Invasion Prediction in Solitary Hepatocellular Carcinoma ≤5 cm Based on CEUS and EOB-MRI: A Retrospective Study with External Validation

RATIONALE AND OBJECTIVES

To develop a model combining contrast-enhanced ultrasound (CEUS) and ethoxybenzyl-enhanced magnetic resonance imaging (EOB-MRI) for predicting microvascular invasion (MVI) in solitary hepatocellular carcinoma (HCC) ≤5 cm.

MATERIALS AND METHODS

Patients between December 2019 and May 2024 in one center were retrospectively enrolled and randomly divided into the training cohort and internal validation cohort in a ratio of 7:3. Patients in a separate center were enrolled between January 2022 and December 2023 to be included as the external validation cohort. CEUS and EOB-MRI image features were extracted and used to develop models in the training cohort, and verified in the two validation cohorts. The predictive accuracy and clinical utility of models were evaluated using area under receiver operating characteristic curve (AUROC), Brier score, calibration plot and decision curve analysis (DCA). Net reclassification index (NRI) and integrated discrimination improvement (IDI) were used to compare different models.

RESULTS

From the two centers a total of 493 patients, of which 134 were MVI positive, were evaluated. The CEUS+EOB model included seven image features and showed better discrimination ability than the individual CEUS/EOB-MRI model, with AUROCs of 0.92, 0.94, and 0.90 in the training cohort and two validation cohorts, respectively (p<0.05). The lowest Brier score of the combined model indicated the highest predictive precision. DCA also showed that the combined model added more net benefits. Both the NRI and IDI values >0 indicated that the combined model had significantly positive improvement (p<0.05).

CONCLUSION

The CEUS+EOB model was developed to assist clinicians in evaluating MVI in solitary HCC ≤5 cm.

Development and validation of a nomogram for predicting prostate cancer based on combining contrast-enhanced transrectal ultrasound and biparametric MRI imaging

Objectives

This study was to explore the feasibility of combining contrast-enhanced transrectal ultrasound (CE-TRUS) with biparametric MRI (CEUS-BpMRI) score for diagnosing prostate cancer (PCa).

Methods

A total of 183 patients with suspected PCa who underwent multiparametric MRI (Mp-MRI) and CE-TRUS were included. CEUS-BpMRI score was developed based on the results of Mp-MRI and CE-TRUS. The diagnostic performance was evaluated by the area under the curve (AUC). The diagnostic efficacy of the CEUS-BpMRI score, BpMRI score, and PI-RADS v2.1 score were compared. Total patients were randomly assigned to a training cohort (70%) or validation cohort (30%). A nomogram was constructed based on univariate and multivariate logistic regression. The model was evaluated by AUC and calibration curve.

Results

The diagnostic performance of CEUS-BpMRI score (AUC 0.857) was comparable to that of PI-RADS v2.1 (AUC 0.862) (P = 0.499), and both were superior to Bp-MRI score (AUC 0.831, P < 0.05). In peripheral zone lesions with Bp-MRI score of 3, there was no statistically significant difference between PI-RADS v2.1 score (AUC 0.728) and CEUS-BpMRI score (AUC 0.668) (P = 0.479). Multivariate analysis showed that age, total prostate specific antigen/free prostate specific antigen (F/T), time to peak (TTP), and CEUS-BpMRI score were independent factors. The AUC of the nomogram was 0.909 in the training cohort and 0.914 in the validation cohort.

Conclusions

CEUS-BpMRI score has high diagnostic efficacy for diagnosing PCa. A nomogram model established by combining age, F/T, TTP, and CEUS-BpMRI score can achieve the best predictive accuracy for PCa.