Preoperative Prediction of Breast Cancer Histological Grade Using Intratumoral and Peritumoral Radiomics Features from T2WI and DWI MR Sequences.

Background: Histological grade is an acknowledged prognostic factor for breast cancer, essential for determining clinical treatment strategies and prognosis assessment. Our study aims to establish intra- and peritumoral radiomics models using T2WI and DWI MR sequences for predicting the histological grade of breast cancer.

Methods: 700 breast cancer cases who had MRI scans before surgery were included.

The Clinical Study of Intratumoral and Peritumoral Radiomics Based on DCE-MRI for HER-2 Positive and Low Expression Prediction in Breast Cancer.

Background: Core biopsy sampling may not fully capture tumor heterogeneity. Radiomics provides a non-invasive method to assess tumor characteristics, including both the core and surrounding tissue, with the potential to improve the accuracy of HER-2 status prediction.

Objective: To explore the clinical value of intratumoral and peritumoral radiomics features from dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) for preoperative prediction of human epidermal growth factor receptor-2 (HER-2) expression status in breast cancer.

Clinical-radiomics nomogram based on the fat-suppressed T2 sequence for differentiating luminal and non-luminal breast cancer.

Objective: To establish and validate a new clinical-radiomics nomogram based on the fat-suppressed T2 sequence for differentiating luminal and non-luminal breast cancer.

Methods: A total of 593 breast cancer patients who underwent preoperative breast MRI from Jan 2017 to Dec 2020 were enrolled, which were randomly divided into the training (n=474) and test sets (n=119) at the ratio of 8:2. Intratumoral region (ITR) of interest were manually delineated, and peritumoral regions of 3 mm and 5 mm (PTR-3 mm and PTR-5 mm) were automatically obtained by dilating the ITR.

Radiomics analysis of intratumoral and different peritumoral regions from multiparametric MRI for evaluating HER2 status of breast cancer: A comparative study.

Purpose: To investigate the potential of radiomics signatures (RSs) from intratumoral and peritumoral regions on multiparametric magnetic resonance imaging (MRI) to noninvasively evaluate HER2 status in breast cancer.

Method: In this retrospective study, 992 patients with pathologically confirmed breast cancers who underwent preoperative MRI were enrolled. The breast cancer lesions were segmented manually, and the intratumor region of interest (ROI) was dilated by 2, 4, 6 and 8 mm (ROI, ROI, ROI, and ROI, respectively).

Clinical study on the prediction of ALN metastasis based on intratumoral and peritumoral DCE-MRI radiomics and clinico-radiological characteristics in breast cancer.

Objective: To investigate the value of predicting axillary lymph node (ALN) metastasis based on intratumoral and peritumoral dynamic contrast-enhanced MRI (DCE-MRI) radiomics and clinico-radiological characteristics in breast cancer.

Methods: A total of 473 breast cancer patients who underwent preoperative DCE-MRI from Jan 2017 to Dec 2020 were enrolled. These patients were randomly divided into training (n=378) and testing sets (n=95) at 8:2 ratio.

An [F]FDG PET/3D-ultrashort echo time MRI-based radiomics model established by machine learning facilitates preoperative assessment of lymph node status in non-small cell lung cancer.

Objectives: To develop an [F]FDG PET/3D-UTE model based on clinical factors, three-dimensional ultrashort echo time (3D-UTE), and PET radiomics features via machine learning for the assessment of lymph node (LN) status in non-small cell lung cancer (NSCLC).

Methods: A total of 145 NSCLC patients (training, 101 cases; test, 44 cases) underwent whole-body [F]FDG PET/CT and chest [F]FDG PET/MRI were enrolled. Preoperative clinical factors and 3D-UTE, CT, and PET radiomics features were analyzed.

Annotation-efficient deep learning for automatic medical image segmentation.

Automatic medical image segmentation plays a critical role in scientific research and medical care. Existing high-performance deep learning methods typically rely on large training datasets with high-quality manual annotations, which are difficult to obtain in many clinical applications. Here, we introduce Annotation-effIcient Deep lEarning (AIDE), an open-source framework to handle imperfect training datasets.

MiR-20a-5p functions as a potent tumor suppressor by targeting PPP6C in acute myeloid leukemia.

Acute myeloid leukemia (AML) is as a highly aggressive and heterogeneous hematological malignancy. MiR-20a-5p has been reported to function as an oncogene or tumor suppressor in several tumors, but the clinical significance and regulatory mechanisms of miR-20a-5p in AML cells have not been fully understood. In this study, we found miR-20a-5p was significantly decreased in bone marrow from AML patients, compared with that in healthy controls.

Radiomics Signatures Based on Multiparametric MRI for the Preoperative Prediction of the HER2 Status of Patients with Breast Cancer.

Objectives: The aim of our study was to preoperatively predict the human epidermal growth factor receptor 2 (HER2) status of patients with breast cancer using radiomics signatures based on single-parametric and multiparametric magnetic resonance imaging (MRI).

Methods: Three hundred six patients with invasive ductal carcinoma of no special type (IDC-NST) were retrospectively enrolled. Quantitative imaging features were extracted from fat-suppressed T2-weighted and dynamic contrast-enhanced T1 weighted (DCE-T1) preoperative MRI.

Mammography-based radiomics nomogram: a potential biomarker to predict axillary lymph node metastasis in breast cancer.

Objective: To establish a radiomics nomogram by integrating clinical risk factors and radiomics features extracted from digital mammography (MG) images for pre-operative prediction of axillary lymph node (ALN) metastasis in breast cancer.

Methods: 216 patients with breast cancer lesions confirmed by surgical excision pathology were divided into the primary cohort ( = 144) and validation cohort ( = 72). Radiomics features were extracted from craniocaudal (CC) view of mammograms, and radiomics features selection were performed using the methods of ANOVA F-value and least absolute shrinkage and selection operator; then a radiomics signature was constructed with the method of support vector machine.

Preoperative Prediction of Axillary Lymph Node Metastasis in Breast Carcinoma Using Radiomics Features Based on the Fat-Suppressed T2 Sequence.

Rationale And Objectives: To investigate the value of radiomics method based on the fat-suppressed T2 sequence for preoperative predicting axillary lymph node (ALN) metastasis in breast carcinoma.

Materials And Methods: The data of 329 invasive breast cancer patients were divided into the primary cohort (n = 269) and validation cohort (n = 60). Radiomics features were extracted from the fat-suppressed T2-weighted images on breast MRI, and ALN metastasis-related radiomics feature selection was performed using Mann-Whitney U-test and support vector machines with recursive feature elimination; then a radiomics signature was constructed by linear support vector machine.

Evaluating the HER-2 status of breast cancer using mammography radiomics features.

Purpose: The aim of our study was to evaluate the HER-2 status in breast cancer patients using mammography (MG) radiomics features.

Methods: A total of 306 Chinese female patients with invasive ductal carcinoma of no special type (IDC-NST) enrolled from January 2013 to July 2018 were divided into a training set (n = 244) and a testing set (n = 62). One hundred and eighty-six radiomics features were extracted from digital MG images based on the training set.

[Quantitative analysis of hepatocellular carcinomas pathological grading in non-contrast magnetic resonance images].

In order to solve the pathological grading of hepatocellular carcinomas (HCC) which depends on biopsy or surgical pathology invasively, a quantitative analysis method based on radiomics signature was proposed for pathological grading of HCC in non-contrast magnetic resonance imaging (MRI) images. The MRI images were integrated to predict clinical outcomes using 328 radiomics features, quantifying tumour image intensity, shape and text, which are extracted from lesion by manual segmentation. Least absolute shrinkage and selection operator (LASSO) were used to select the most-predictive radiomics features for the pathological grading.

Fully Convolutional DenseNet with Multiscale Context for Automated Breast Tumor Segmentation.

Breast tumor segmentation plays a crucial role in subsequent disease diagnosis, and most algorithms need interactive prior to firstly locate tumors and perform segmentation based on tumor-centric candidates. In this paper, we propose a fully convolutional network to achieve automatic segmentation of breast tumor in an end-to-end manner. Considering the diversity of shape and size for malignant tumors in the digital mammograms, we introduce multiscale image information into the fully convolutional dense network architecture to improve the segmentation precision.

Multi-level features combined end-to-end learning for automated pathological grading of breast cancer on digital mammograms.

We propose to discriminate the pathological grades directly on digital mammograms instead of pathological images. An end-to-end learning algorithm based on the combined multi-level features is proposed. Low-level features are extracted and selected by supervised LASSO logistic regression.

Predicting the grade of hepatocellular carcinoma based on non-contrast-enhanced MRI radiomics signature.

Purpose: This study was conducted in order to investigate the value of magnetic resonance imaging (MRI)-based radiomics signatures for the preoperative prediction of hepatocellular carcinoma (HCC) grade.

Methods: Data from 170 patients confirmed to have HCC by surgical pathology were divided into a training group (n = 125) and a test group (n = 45). The radiomics features of tumours based on both T1-weighted imaging (WI) and T2WI were extracted by using Matrix Laboratory (MATLAB), and radiomics signatures were generated using the least absolute shrinkage and selection operator (LASSO) logistic regression model.

TP53TG1 enhances cisplatin sensitivity of non-small cell lung cancer cells through regulating miR-18a/PTEN axis.

Background: The acquisition of drug resistance has been considered as a main obstacle for cancer chemotherapy. Tumor protein 53 target gene 1 (TP53TG1), a p53-induced lncRNA, plays a vital role in the progression of human cancers. However, little is known about the detailed function and molecular mechanism of TP53TG1 in cisplatin resistance of NSCLC.

The use of sequential X-ray, CT and MRI in the preoperative evaluation of breast-conserving surgery.

The aim of the study was to investigate the value of sequential application of molybdenum target X-ray, multi-slice spiral computed tomography (MSCT) and magnetic resonance imaging (MRI) in the preoperative evaluation of breast-conserving surgeries. In total, 76 patients with indications for breast-conserving surgery due to complicated breast cancer participated in the study and were assigned to either control or observation group (n=38 per group). The patients in the control group were evaluated with two sets of random combinations of molybdenum target X-ray, MSCT or MRI with ultrasound inspection, whereas the patients in the observation group were evaluated by sequential inspection methods of molybdenum target X-ray, MSCT and MRI.

Radiologic and Clinicopathologic Findings of Inflammatory Myofibroblastic Tumor.

Purpose: The aim of the study was to describe the clinical, radiographic, and pathologic features of inflammatory myofibroblastic tumor (IMT) to enhance the recognition of this rare disease.

Materials And Methods: The clinical, imaging, and pathologic findings were retrospectively reviewed in 54 patients with IMT lesions, which were conformed by biopsy or surgical pathology. Of 54 patients, 51 had preoperative computed tomography (CT) examination and 13 had preoperative magnetic resonance imaging records.

CT and clinical findings of peripheral primitive neuroectodermal tumour in children.

Objective: To describe the clinical, CT and pathological findings of paediatric peripheral primitive neuroectodermal tumours (pPNETs) to enhance the recognition of these rare tumours.

Methods: The clinical, CT and pathological findings of 18 paediatric patients with pPNETs confirmed by biopsy or surgical pathology were retrospectively reviewed.

Results: The age of these 18 paediatric patients with pPNETs ranged from 4 months to 15 years, with a mean age of 7.

[The best noise index combined with ASIR weighting selection in low-dose chest scanning].

Objective: To discuss the best noise index combined with ASIR weighting selection in low-dose chest scanning based on BMI.

Methods: 200 patients collected from May to December 2014 underwent non-contrast chest CT examinations, they were randomly assigned into standard dose group (Group A, NI15 combined with 30% ASIR) and low-dose groups (Group B, NI25 combined with 40% ASIR, Group C, NI30 combined with 50% ASIR, Group D, NI35 combined with 60% ASIR), 50 cases in each group; the patients were assigned into three groups based on BMI (kg/m2): BMI<18.5; 18.

A pilot study using low-dose Spectral CT and ASIR (Adaptive Statistical Iterative Reconstruction) algorithm to diagnose solitary pulmonary nodules.

Background: Lung cancer is the most common cancer which has the highest mortality rate. With the development of computed tomography (CT) techniques, the case detection rates of solitary pulmonary nodules (SPN) has constantly increased and the diagnosis accuracy of SPN has remained a hot topic in clinical and imaging diagnosis. The aim of this study was to evaluate the combination of low-dose spectral CT and ASIR (Adaptive Statistical Iterative Reconstruction) algorithm in the diagnosis of solitary pulmonary nodules (SPN).

Pediatric Cryptococcal Lymphadenitis in the Absence of AIDS: Case Report and Literature Review.

We present a rare case of cryptococcal lymphadenitis without immunocompromization in a two-and-a-half-year-old child. He was referred to our center with a fifteen-day history of continued fever. Ultrasound and computed tomography (CT) revealed the enlargement of multiple lymph nodes and lung reticulonodular shadows.