Radiomics of multimodal ultrasound for early prediction of pathologic complete response to neoadjuvant chemotherapy in breast cancer.

Rationale And Objectives: To construct and validate a clinical-radiomics model based on radiomics features extracted from two-stage multimodal ultrasound and clinicopathologic information for early predicting pathologic complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer patients treated with NAC.

Materials And Methods: Consecutive women with biopsy-proven breast cancer undergoing multimodal US pretreatment and after two cycles of NAC and followed by surgery between January 2014 and November 2023 were retrospectively collected for clinical-radiomics model construction (n = 274) and retrospective test (n = 134). The predictive performance of it was further tested in a subsequent prospective internal test set recruited between January 2024 to July 2024 (n = 76).

Automatic assessment of DWI-ASPECTS for acute ischemic stroke based on deep learning.

Background: Alberta Stroke Program Early Computed Tomography Score (ASPECTS) is a standardized semi-quantitative method for early ischemic changes in acute ischemic stroke.

Purpose: However, ASPECTS is still affected by expert experience and inconsistent results between readers in clinical. This study aims to propose an automatic ASPECTS scoring model based on diffusion-weighted imaging (DWI) mode to help clinicians make accurate treatment plans.

Weakly Supervised Breast Lesion Detection in Dynamic Contrast-Enhanced MRI.

Currently, obtaining accurate medical annotations requires high labor and time effort, which largely limits the development of supervised learning-based tumor detection tasks. In this work, we investigated a weakly supervised learning model for detecting breast lesions in dynamic contrast-enhanced MRI (DCE-MRI) with only image-level labels. Two hundred fifty-four normal and 398 abnormal cases with pathologically confirmed lesions were retrospectively enrolled into the breast dataset, which was divided into the training set (80%), validation set (10%), and testing set (10%) at the patient level.

Ischemic stroke subtyping method combining convolutional neural network and radiomics.

Background: Cardiogenic embolism (CE) and large-artery atherosclerosis embolism (LAA) are the two most common ischemic stroke (IS) subtypes.

Objective: In order to assist doctors in the precise diagnosis and treatment of patients, this study proposed an IS subtyping method combining convolutional neural networks (CNN) and radiomics.

Methods: Firstly, brain embolism regions were segmented from the computed tomography angiography (CTA) images, and radiomics features were extracted; Secondly, the extracted radiomics features were optimized with the L2 norm, and the feature selection was performed by combining random forest; then, the CNN Cap-UNet was built to extract the deep learning features of the last layer of the network; Finally, combining the selected radiomics features and deep learning features, 9 small-sample classifiers were trained respectively to build and select the optimal IS subtyping classification model.

Automated Quantitative Analysis of Blood Flow in Extracranial-Intracranial Arterial Bypass Based on Indocyanine Green Angiography.

Microvascular imaging based on indocyanine green is an important tool for surgeons who carry out extracranial-intracranial arterial bypass surgery. In terms of blood perfusion, indocyanine green images contain abundant information, which cannot be effectively interpreted by humans or currently available commercial software. In this paper, an automatic processing framework for perfusion assessments based on indocyanine green videos is proposed and consists of three stages, namely, vessel segmentation based on the UNet deep neural network, preoperative and postoperative image registrations based on scale-invariant transform features, and blood flow evaluation based on the Horn-Schunck optical flow method.

The complete mitochondrial genome of the blue-crested lizard, (Squamata, Agamidae) in China.

A complete mitogenome sequence of the blue-crested lizard () was determined in this study. The 16,506 bp genome consists 13 protein-coding genes (PCG), two ribosomal RNA (rRNA) genes, and 22 transfer RNA (tRNA) genes, and a control region. The phylogenetic tree reveals that the is closely related to the .

The complete mitogenome of the omei toothed toad, (Anura: Megophryidae) with phylogenetic analysis.

We sequenced and annotated the complete mitogenome sequence of (17,675 bp long). The mitogenome encoded 13 protein-coding genes (PCG), 2 ribosomal RNA (rRNA) genes, 23 transfer RNA (tRNA) genes, and a control region (GenBank accession number MN803321). The phylogenetic tree conforms the close relationship of and and a monophyletic clade of genus .

Automatic detection of intracranial aneurysms in 3D-DSA based on a Bayesian optimized filter.

Background: Intracranial aneurysm is a common type of cerebrovascular disease with a risk of devastating subarachnoid hemorrhage if it is ruptured. Accurate computer-aided detection of aneurysms can help doctors improve the diagnostic accuracy, and it is very helpful in reducing the risk of subarachnoid hemorrhage. Aneurysms are detected in 2D or 3D images from different modalities.

Transfer learning radiomics based on multimodal ultrasound imaging for staging liver fibrosis.

Objectives: To propose a transfer learning (TL) radiomics model that efficiently combines the information from gray scale and elastogram ultrasound images for accurate liver fibrosis grading.

Methods: Totally 466 patients undergoing partial hepatectomy were enrolled, including 401 with chronic hepatitis B and 65 without fibrosis pathologically. All patients received elastography and got liver stiffness measurement (LSM) 2-3 days before surgery.