Comparative Study of C-TIRADS, ACR-TIRADS, and EU-TIRADS for Diagnosis and Management of Thyroid Nodules.

Rationale And Objectives: Chinese Thyroid Imaging Reporting and Data Systems (C-TIRADS) was developed to provide a more simplified tool for stratifying thyroid nodules. Here we aimed to validate the efficacy of C-TIRADS in distinguishing benign from malignant and in guiding fine-needle aspiration biopsies in comparison with the American College of Radiology TIRADS (ACR-TIRADS) and European TIRADS (EU-TIRADS).

Materials And Methods: This study retrospectively included 3438 thyroid nodules (≥10 mm) in 3013 patients (mean age, 47.

Machine learning-based multimodal MRI texture analysis for assessing renal function and fibrosis in diabetic nephropathy: a retrospective study.

Introduction: Diabetic nephropathy (DN) has become a major public health burden in China. A more stable method is needed to reflect the different stages of renal function impairment. We aimed to determine the possible practicability of machine learning (ML)-based multimodal MRI texture analysis (mMRI-TA) for assessing renal function in DN.

MRI texture-based machine learning models for the evaluation of renal function on different segmentations: a proof-of-concept study.

Background: To develop and validate an MRI texture-based machine learning model for the noninvasive assessment of renal function.

Methods: A retrospective study of 174 diabetic patients (training cohort, n = 123; validation cohort, n = 51) who underwent renal MRI scans was included. They were assigned to normal function (n = 71), mild or moderate impairment (n = 69), and severe impairment groups (n = 34) according to renal function.

Thy-Wise: An interpretable machine learning model for the evaluation of thyroid nodules.

Current risk stratification systems for thyroid nodules suffer from low specificity and high biopsy rates. Recently, machine learning (ML) is introduced to assist thyroid nodule diagnosis but lacks interpretability. Here, we developed and validated ML models on 3965 thyroid nodules, as compared to the American College of Radiology Thyroid Imaging, Reporting and Data System (ACR TI-RADS).

Real-time automatic prediction of treatment response to transcatheter arterial chemoembolization in patients with hepatocellular carcinoma using deep learning based on digital subtraction angiography videos.

Background: Transcatheter arterial chemoembolization (TACE) is the mainstay of therapy for intermediate-stage hepatocellular carcinoma (HCC); yet its efficacy varies between patients with the same tumor stage. Accurate prediction of TACE response remains a major concern to avoid overtreatment. Thus, we aimed to develop and validate an artificial intelligence system for real-time automatic prediction of TACE response in HCC patients based on digital subtraction angiography (DSA) videos via a deep learning approach.

Radiogenomic association between the T2-FLAIR mismatch sign and IDH mutation status in adult patients with lower-grade gliomas: an updated systematic review and meta-analysis.

Objectives: To reveal a radiogenomic correlation between the presence of the T2-fluid-attenuated inversion recovery resection (T2-FLAIR) mismatch sign on MR images and isocitrate dehydrogenase (IDH) mutation status in adult patients with lower-grade gliomas (LGGs).

Methods: A web-based systemic search for eligible literature up to April 13, 2021, was conducted on PubMed, Embase, and the Cochrane Library databases by two independent reviewers. This study was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines.

Current status and quality of radiomic studies for predicting immunotherapy response and outcome in patients with non-small cell lung cancer: a systematic review and meta-analysis.

Purpose: Prediction of immunotherapy response and outcome in patients with non-small cell lung cancer (NSCLC) is challenging due to intratumoral heterogeneity and lack of robust biomarkers. The aim of this study was to systematically evaluate the methodological quality of radiomic studies for predicting immunotherapy response or outcome in patients with NSCLC.

Methods: We systematically searched for eligible studies in the PubMed and Web of Science datasets up to April 1, 2021.

Predicting Intensive Care Unit Length of Stay After Acute Type A Aortic Dissection Surgery Using Machine Learning.

Patients with acute type A aortic dissection are usually transferred to the intensive care unit (ICU) after surgery. Prolonged ICU length of stay (ICU-LOS) is associated with higher level of care and higher mortality. We aimed to develop and validate machine learning models for predicting ICU-LOS after acute type A aortic dissection surgery.

Quantitative MRI-based radiomics for noninvasively predicting molecular subtypes and survival in glioma patients.

Gliomas can be classified into five molecular groups based on the status of IDH mutation, 1p/19q codeletion, and TERT promoter mutation, whereas they need to be obtained by biopsy or surgery. Thus, we aimed to use MRI-based radiomics to noninvasively predict the molecular groups and assess their prognostic value. We retrospectively identified 357 patients with gliomas and extracted radiomic features from their preoperative MRI images.

Concurrent chemoradiotherapy with additional chemotherapy for nasopharyngeal carcinoma: A pooled analysis of propensity score-matching studies.

Objective: To determine the benefits of adding induction chemotherapy (IC) and adjuvant chemotherapy (AC) to concurrent chemoradiotherapy (CCRT) for nasopharyngeal carcinoma (NPC) based on propensity score-matching (PSM) studies.

Methods: Eligible PSM studies were searched in the PubMed, Web of Science, and Embase databases from inception to September 1, 2020. The primary endpoints included overall survival (OS), distant metastasis-free survival (DMFS), and locoregional recurrence-free survival (LRFS).

Machine learning classifiers for predicting 3-year progression-free survival and overall survival in patients with gliomas after surgery.

To develop machine-learning based models to predict the progression-free survival (PFS) and overall survival (OS) in patients with gliomas and explore the effect of different feature selection methods on the prediction. We included 505 patients (training cohort, n = 354; validation cohort, n = 151) with gliomas between January 1, 2011 and December 31, 2016. The clinical, neuroimaging, and molecular genetic data of patients were retrospectively collected.

Predicting hyperprogressive disease in patients with advanced hepatocellular carcinoma treated with anti-programmed cell death 1 therapy.

Background: Hyperprogressive disease (HPD) is a new progressive pattern in patients with advanced hepatocellular carcinoma (HCC) treated with programmed cell death 1 (PD-1) inhibitors. We aimed to investigate risk factors associated with HPD in advanced HCC patients undergoing anti-PD-1 therapy.

Methods: A total of 69 patients treated with anti-PD-1 therapy between March 2017 and January 2020 were included.

Immune-checkpoint inhibitor plus chemotherapy conventional chemotherapy for treatment of recurrent or metastatic head and neck squamous cell carcinoma: a systematic review and network meta-analysis.

Background: Multiple therapies including immune-checkpoint inhibitors are emerging as effective treatment for patients with recurrent or metastatic head and neck squamous cell carcinoma (R/M HNSSC). However, the optimal first-line and second-line treatments remains controversial.

Methods: We systematically searched databases and conducted a systematic review of phase II/III randomized controlled trials (RCTs) that compared two or more treatments for R/M HNSSC.

MRI-Based Deep-Learning Model for Distant Metastasis-Free Survival in Locoregionally Advanced Nasopharyngeal Carcinoma.

Background: Distant metastasis is the primary cause of treatment failure in locoregionally advanced nasopharyngeal carcinoma (LANPC).

Purpose: To develop a model to evaluate distant metastasis-free survival (DMFS) in LANPC and to explore the value of additional chemotherapy to concurrent chemoradiotherapy (CCRT) for different risk groups.

Study Type: Retrospective.

Machine-learning based MRI radiomics models for early detection of radiation-induced brain injury in nasopharyngeal carcinoma.

Background: Early radiation-induced temporal lobe injury (RTLI) diagnosis in nasopharyngeal carcinoma (NPC) is clinically challenging, and prediction models of RTLI are lacking. Hence, we aimed to develop radiomic models for early detection of RTLI.

Methods: We retrospectively included a total of 242 NPC patients who underwent regular follow-up magnetic resonance imaging (MRI) examinations, including contrast-enhanced T1-weighted and T2-weighted imaging.

Development and validation of a Web-based malignancy risk-stratification system of thyroid nodules.

Objectives: Previous publications on risk-stratification systems for malignant thyroid nodules were based on conventional ultrasound only. We aimed to develop a practical and simplified prediction model for categorizing the malignancy risk of thyroid nodules based on clinical data, biochemical data, conventional ultrasound and real-time elastography.

Design: Retrospective cohort study.

Evaluating primary intra-arterial chemotherapy versus intravenous plus intra-arterial chemotherapy for advanced intraocular retinoblastoma.

Purpose: Although intra-arterial chemotherapy (IAC) is commonly used for treating intraocular retinoblastoma, it is not a systemic therapy. We aimed to investigate whether the addition of intravenous chemotherapy (IVC) before IAC administration had any effects (whether beneficial or adverse) on patient outcomes.

Methods: This multicenter retrospective cohort study included 213 patients with advanced intraocular retinoblastoma who received IVC plus IAC (n = 103) or IAC alone (n = 110) between April 2009 and January 2017.

Prognostic value of the radiomics-based model in progression-free survival of hypopharyngeal cancer treated with chemoradiation.

Purpose: To develop a radiomics-based model to stratify the risk of early progression (local/regional recurrence or metastasis) among patients with hypopharyngeal cancer undergoing chemoradiotherapy and modify their pretreatment plans.

Materials And Methods: We randomly assigned 113 patients into two cohorts: training (n = 80) and validation (n = 33). The radiomic significant features were selected in the training cohort using least absolute shrinkage and selection operator and Akaike information criterion methods, and they were used to build the radiomic model.

Radiomic Nomogram: Pretreatment Evaluation of Local Recurrence in Nasopharyngeal Carcinoma based on MR Imaging.

: To develop and validate a radiomic nomogram incorporating radiomic features with clinical variables for individual local recurrence risk assessment in nasopharyngeal carcinoma (NPC) patients before initial treatment. : One hundred and forty patients were randomly divided into a training cohort (n = 80) and a validation cohort (n = 60). A total of 970 radiomic features were extracted from pretreatment magnetic resonance (MR) images of NPC patients from May 2007 to December 2013.

Intra-arterial chemotherapy as primary or secondary treatment for infants diagnosed with advanced retinoblastoma before 3 months of age.

Background: To evaluate the safety and efficacy of intra-arterial chemotherapy (IAC) for the primary or secondary treatment of infants diagnosed with advanced retinoblastoma before 3 months of age.

Methods: This single-center retrospective study included 39 infants (42 eyes) aged ≤3 months who were diagnosed with unilateral or bilateral advanced intraocular retinoblastoma (group D and E eyes) and received IAC as primary or secondary treatment between June 2012 and February 2017. Based on each patient's therapeutic history and response to chemotherapeutic drugs, melphalan, topotecan, and/or carboplatin were used for IAC.

Insulin use and fracture risk in patients with type 2 diabetes: A meta-analysis of 138,690 patients.

Patients with type 2 diabetes mellitus (T2DM) have been reported to have an enhanced risk of bone fracture, however the association between insulin treatment and risk of fracture in patients with T2DM remains to be fully elucidated. The aim of the present meta-analysis was to examine the possible association between insulin treatment and risk of fracture in patients with T2DM. Relevant studies published prior to and including April 2018 were identified by literature searches in PubMed, Embase and Cochrane Library databases.

Machine Learning-Assisted System for Thyroid Nodule Diagnosis.

Ultrasound (US) examination is helpful in the differential diagnosis of thyroid nodules (malignant vs. benign), but its accuracy relies heavily on examiner experience. Therefore, the aim of this study was to develop a less subjective diagnostic model aided by machine learning.

Development and validation of a magnetic resonance imaging-based model for the prediction of distant metastasis before initial treatment of nasopharyngeal carcinoma: A retrospective cohort study.

Background: We aimed to identify a magnetic resonance imaging (MRI)-based model for assessment of the risk of individual distant metastasis (DM) before initial treatment of nasopharyngeal carcinoma (NPC).

Methods: This retrospective cohort analysis included 176 patients with NPC. Using the PyRadiomics platform, we extracted the imaging features of primary tumors in all patients who did not exhibit DM before treatment.

[Development and validation of a multivariate risk model for distant metastasis of advanced nasopharyngeal carcinoma].

Objective: To develop a model based on the clinical variables for evaluating the risk of distant metastasis in patients with advanced nasopharyngeal carcinoma (NPC).

Methods: From September,2007 to June,2015,a total of 238 consecutive patients with biopsy-proven NPC in stage Ⅲ-Ⅳ(M0) based on the AJCC TNM staging manual were enrolled in this study,including 106 male and 34 female patients with a median age of 45 years (range 18-68 years).In this cohort,126 patients received concurrent chemoradiotherapy,and 24 received chemotherapy and radiotherapy,and 40 had induction chemotherapy.