Artificial intelligence-augmented ultrasound diagnosis of follicular-patterned thyroid neoplasms: a multicenter retrospective study.

Background: Conventional diagnostic tools, including ultrasound, fine-needle aspiration cytology, and intraoperative frozen section pathology, may fail to reliably distinguish between benign and malignant FNs, leading to unnecessary or inadequate surgical interventions. We aimed to develop and validate a deep learning (DL) system for the preoperative diagnosis of follicular-patterned thyroid neoplasms (FNs) using routine ultrasound images, with the goal of improving diagnostic accuracy and reducing unnecessary procedures.

Methods: In this multicenter, retrospective study, we included 3817 patients (2877 [75.

Noninvasive Deep Learning System for Preoperative Diagnosis of Follicular-Like Thyroid Neoplasms Using Ultrasound Images: A Multicenter, Retrospective Study.

Objective: To propose a deep learning (DL) system for the preoperative diagnosis of follicular-like thyroid neoplasms (FNs) using routine ultrasound images.

Summary Background Data: Preoperative diagnosis of malignancy in nodules suspicious for an FN remains challenging. Ultrasound, fine-needle aspiration cytology, and intraoperative frozen section pathology cannot unambiguously distinguish between benign and malignant FNs, leading to unnecessary biopsies and operations in benign nodules.

Radiomic Model Associated with Tumor Microenvironment Predicts Immunotherapy Response and Prognosis in Patients with Locoregionally Advanced Nasopharyngeal Carcinoma.

No robust biomarkers have been identified to predict the efficacy of programmed cell death protein 1 (PD-1) inhibitors in patients with locoregionally advanced nasopharyngeal carcinoma (LANPC). We aimed to develop radiomic models using pre-immunotherapy MRI to predict the response to PD-1 inhibitors and the patient prognosis. This study included 246 LANPC patients (training cohort, = 117; external test cohort, = 129) from 10 centers.

Artificial Intelligence Can Predict Personalized Immunotherapy Outcomes in Cancer.

The rapid advancement of artificial intelligence (AI) technologies has opened new avenues for advancing personalized immunotherapy in cancer treatment. This review highlights current research progress in applying AI to optimize the use of immunotherapy for patients with cancer. Recent studies demonstrate that AI models can accurately diagnose cancers and discover biomarkers by integrating multi-omics and imaging data, establish predictive models to estimate treatment responses and adverse reactions, formulate personalized treatment plans integrating multiple modalities by considering various factors, and achieve precise patient stratification and clinical trial matching, thereby addressing specific obstacles throughout processes from diagnosis to treatment in personalized immunotherapy.

Radiogenomics: bridging the gap between imaging and genomics for precision oncology.

Genomics allows the tracing of origin and evolution of cancer at molecular scale and underpin modern cancer diagnosis and treatment systems. Yet, molecular biomarker-guided clinical decision-making encounters major challenges in the realm of individualized medicine, consisting of the invasiveness of procedures and the sampling errors due to high tumor heterogeneity. By contrast, medical imaging enables noninvasive and global characterization of tumors at a low cost.

Prediction of Glioma enhancement pattern using a MRI radiomics-based model.

Contrast-MRI scans carry risks associated with the chemical contrast agents. Accurate prediction of enhancement pattern of gliomas has potential in avoiding contrast agent administration to patients. This study aimed to develop a machine learning radiomics model that can accurately predict enhancement pattern of gliomas based on T2 fluid attenuated inversion recovery images.

Machine Learning-Based Multiparametric Magnetic Resonance Imaging Radiomic Model for Discrimination of Pathological Subtypes of Craniopharyngioma.

Background: Preoperative, noninvasive discrimination of the craniopharyngioma subtypes is important because it influences the treatment strategy.

Purpose: To develop a radiomic model based on multiparametric magnetic resonance imaging for noninvasive discrimination of pathological subtypes of craniopharyngioma.

Study Type: Retrospective.

The Added Value of Inflow-Based Vascular-Space-Occupancy and Diffusion-Weighted Imaging in Preoperative Grading of Gliomas.

Objectives: The present study aimed to study whether combined inflow-based vascular-space-occupancy (iVASO) MR imaging (MRI) and diffusion-weighted imaging (DWI) improve the diagnostic accuracy in the preoperative grading of gliomas.

Methods: Fifty-one patients with histopathologically confirmed diffuse gliomas underwent preoperative structural MRI, iVASO, and DWI. We performed 2 qualitative consensus reviews: (1) structural MR images alone and (2) structural MR images with iVASO and DWI.

Noninvasive Assessment of O(6)-Methylguanine-DNA Methyltransferase Promoter Methylation Status in World Health Organization Grade II-IV Glioma Using Histogram Analysis of Inflow-Based Vascular-Space-Occupancy Combined with Structural Magnetic Resonance Imaging.

Background: O(6)-methylguanine-DNA methyltransferase (MGMT) promoter methylation is an important prognostic factor for gliomas and is associated with tumor angiogenesis. Arteriolar cerebral blood volume (CBVa) obtained from inflow-based vascular-space-occupancy (iVASO) magnetic resonance imaging (MRI) is assumed to be an indicator of tumor microvasculature. Its preoperative predictive ability for MGMT promoter methylation remains unclear.

Conventional MRI Features of Central Nervous System Embryonal Tumor, Not Otherwise Specified in Adults: Comparison with Glioblastoma.

Rationale And Objectives: The purpose of this study was to explore conventional MRI features that can accurately differentiate central nervous system embryonal tumor, not otherwise specified (CNS ETNOS) from glioblastoma (GBM) in adults.

Materials And Methods: Preoperative conventional MRI images of 30 CNS ETNOS and 98 GBMs were analyzed by neuroradiologists retrospectively to identify valuable MRI features. Five blinded neuroradiologists independently reviewed all these MRI images, and scored MRI features on a five-point scale.

Clivopalate angle: a new diagnostic method for basilar invagination at magnetic resonance imaging.

Objectives: To investigate the diagnostic value of clivopalate angle (CPA) for basilar invagination (BI) at magnetic resonance imaging (MRI).

Methods: In this retrospective case-control study, CPA, clivodens angle (CDA), and clivoaxial angle (CXA) were measured on midsagittal MR images from 112 patients with BI (22 men; mean age, 43.9 years ± 13.