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Текущие прикладные программы снимков, радиомедики и методы машинного обучения CNS демиелионирующих расстройств и их имитаторов.

Различие между нейро-воспалительными заболеваниями центральной нервной системы может представлять собой серьезную диагностическую проблему, обусловленную значительным дублированием клинических презентаций и изображений. Сотрудничество между специалистами, новые испытания антител и специальные протоколы магнитного резонанса помогли сократить диагностический разрыв, но сложные случаи остаются. Алгоритмы машинного обучения доказали, что они способны определить тонкие закономерности, которые избегают даже самых опытных человеческих глаз. В самом деле, машинное обучение и подполе радиотехники проде

Совпадение в аннотации оригинала
Distinguishing among neuroinflammatory demyelinating diseases of the central nervous system can present a significant diagnostic challenge d … terns that escape even the most experienced human eye. Indeed, machine learning and the subfield of radiomics have demonstrated exponential growth and improvement in diagnosis capacity within the past decade. The sometimes daunting diagnostic overlap of various demyelinating processes thus provides a unique opportunity: can the elite pattern recognition pow …

Distinguishing EGFR mutation molecular subtypes based on MRI radiomics features of lung adenocarcinoma brain metastases.

OBJECTIVE: To explore the feasibility of identifying epidermal growth factor receptor (EGFR) mutation molecular subtypes in primary lesions based on the radiomics features of lung adenocarcinoma brain metastases using magnetic resonance imaging (MRI). METHODS: We retrospectively analyzed clinical, imaging, and genetic testing data of patients with lung adenocarcinoma with EGFR gene mutations who had brain metastases. Three-dimensional radiomics features were extracted from contrast-enhanced T1-weighted images. The volume of interest was delineated and normalized using Z-score, dimensionality …

Habitat-Based Radiomics for Predicting EGFR Mutations in Exon 19 and 21 From Brain Metastasis.

RATIONALE AND OBJECTIVES: To explore and externally validate habitat-based radiomics for preoperative prediction of epidermal growth factor receptor (EGFR) mutations in exon 19 and 21 from MRI imaging of non-small cell lung cancer (NSCLC)-originated brain metastasis (BM). METHODS: A total of 170, 62 and 61 patients from center 1, center 2 and center 3, respectively were included. All patients underwent contrast-enhanced T1-weighted (T1CE) and T2-weighted (T2W) MRI scans. Radiomics features were extracted from the tumor active (TA) and peritumoral edema (PE) regions in each MRI slice. The most…

Evaluating complete response prediction rates in locally advanced rectal cancer with different radiomics segmentation approaches.

PURPOSE: Studies examining prediction of complete response (CR) in locally advanced rectum cancer (LARC) from pre/post chemoradiotherapy (CRT) magnetic resonance imaging (MRI) are performed mostly with segmentations of the tumor, whereas only in two studies segmentation included tumor and mesorectum. Additionally, pelvic extramesorectal region, which is included in the clinical target volume (CTV) of radiotherapy, may contain information. Therefore, we aimed to compare predictive rates of radiomics analysis with features extracted from segmentations of tumor, tumor+mesorectum, and CTV. METHOD…

MRI-based radiomics models predict cystic brain radionecrosis of nasopharyngeal carcinoma after intensity modulated radiotherapy.

OBJECTIVE: To construct radiomics models based on MRI at different time points for the early prediction of cystic brain radionecrosis (CBRN) for nasopharyngeal carcinoma (NPC). METHODS: A total of 202 injured temporal lobes from 155 NPC patients with radiotherapy-induced temporal lobe injury (RTLI) after intensity modulated radiotherapy (IMRT) were included in the study. All the injured lobes were randomly divided into the training (n = 143) and validation (n = 59) sets. Radiomics models were constructed by using features extracted from T2WI at two different time points: at the end of IMRT (p…

Delta radiomics to track radiation response in lung tumors receiving stereotactic magnetic resonance-guided radiotherapy.

BACKGROUND AND PURPOSE: Lung cancer is a leading cause of cancer-related mortality, and stereotactic body radiotherapy (SBRT) has become a standard treatment for early-stage lung cancer. However, the heterogeneous response to radiation at the tumor level poses challenges. Currently, standardized dosage regimens lack adaptation based on individual patient or tumor characteristics. Thus, we explore the potential of delta radiomics from on-treatment magnetic resonance (MR) imaging to track radiation dose response, inform personalized radiotherapy dosing, and predict outcomes. MATERIALS AND METHO…

Noninvasive Radiomics Approach Predicts Dopamine Agonists Treatment Response in Patients with Prolactinoma: A Multicenter Study.

RATIONALE AND OBJECTIVES: The first-line treatment for prolactinoma is drug therapy with dopamine agonists (DAs). However, some patients with resistance to DA treatment should prioritize surgical treatment. Therefore, it is crucial to accurately identify the drug treatment response of prolactinoma before treatment. The present study was performed to determine the DA treatment response of prolactinoma using a clinical radiomic model that incorporated radiomic and clinical features before treatment. MATERIALS AND METHODS: In total, 255 patients diagnosed with prolactinoma were retrospectively d

Role of peritumoral tissue analysis in predicting characteristics of hepatocellular carcinoma using ultrasound-based radiomics.

Predicting the biological characteristics of hepatocellular carcinoma (HCC) is essential for personalized treatment. This study explored the role of ultrasound-based radiomics of peritumoral tissues for predicting HCC features, focusing on differentiation, cytokeratin 7 (CK7) and Ki67 expression, and p53 mutation status. A cohort of 153 patients with HCC underwent ultrasound examinations and radiomics features were extracted from peritumoral tissues. Subgroups were formed based on HCC characteristics. Predictive modeling was carried out using the XGBOOST algorithm in the differentiation subgro…

Clinical utility of an artificial intelligence radiomics-based tool for risk stratification of pulmonary nodules.

BACKGROUND: Clinical utility data on pulmonary nodule (PN) risk stratification biomarkers are lacking. We aimed to determine the incremental predictive value and clinical utility of using an artificial intelligence (AI) radiomics-based computer-aided diagnosis (CAD) tool in addition to routine clinical information to risk stratify PNs among real-world patients. METHODS: We performed a retrospective cohort study of patients with PNs who underwent lung biopsy. We collected clinical data and used a commercially available AI radiomics-based CAD tool to calculate a Lung Cancer Prediction (LCP) sco…

CT-derived radiomics predict the growth rate of renal tumours in von Hippel-Lindau syndrome.

AIM: To predict renal tumour growth patterns in von Hippel-Lindau syndrome by utilising radiomic features to assist in developing personalised surveillance plans leading to better patient outcomes. MATERIALS AND METHODS: The study evaluated 78 renal tumours in 55 patients with histopathologically-confirmed clear cell renal cell carcinomas (ccRCCs), which were segmented and radiomics were extracted. Volumetric doubling time (VDT) classified the tumours into fast-growing (VDT <365 days) or slow-growing (VDT ≥365 days). Volumetric and diametric growth analyses were compared between the groups. M…

Accuracy of Radiomics in Predicting IDH Mutation Status in Diffuse Gliomas: A Bivariate Meta-Analysis.

Purpose To perform a systematic review and meta-analysis assessing the predictive accuracy of radiomics in the noninvasive determination of isocitrate dehydrogenase (IDH) status in grade 4 and lower-grade diffuse gliomas. Materials and Methods A systematic search was performed in the PubMed, Scopus, Embase, Web of Science, and Cochrane Library databases for relevant articles published between January 1, 2010, and July 7, 2021. Pooled sensitivity and specificity across studies were estimated. Risk of bias was evaluated using Quality Assessment of Diagnostic Accuracy Studies-2, and methods were …

Novel Functional Radiomics for Prediction of Cardiac Positron Emission Tomography Avidity in Lung Cancer Radiotherapy.

PURPOSE: Traditional methods of evaluating cardiotoxicity focus on radiation doses to the heart. Functional imaging has the potential to provide improved prediction for cardiotoxicity for patients with lung cancer. Fluorine-18 (18F) fluorodeoxyglucose (FDG)-positron emission tomography (PET)/computed tomography (CT) imaging is routinely obtained in a standard cancer staging workup. This work aimed to develop a radiomics model predicting clinical cardiac assessment using 18F-FDG PET/CT scans before thoracic radiation therapy. METHODS: Pretreatment 18F-FDG PET/CT scans from three study populati…

Placental T2-weighted MRI-based radiomics-clinical nomogram to predict postpartum hemorrhage of placenta previa.

BACKGROUND: Placenta previa is an obstetric complication related to severe maternal morbidity and mortality. Magnetic resonance imaging (MRI) can be used for the preoperative evaluation of postpartum hemorrhage. PURPOSE: To investigate the value of MRI-based radiomics analysis in predicting postpartum hemorrhage among pregnant women with placenta previa. MATERIAL AND METHODS: Preoperative T2-weighted MRI and related clinical data of 371 patients were retrospectively collected, and these patients were randomly allocated into two subsets: the training dataset (n = 260) and the validation datas…

Differentiation of pheochromocytoma and adrenal lipoid adenoma by radiomics: are enhanced CT scanning images necessary?

PURPOSE: To establish various radiomics models based on conventional CT scan images and enhanced CT images, explore their value in the classification of pheochromocytoma (PHEO) and lipid-poor adrenal adenoma (LPA) and screen the most parsimonious and efficient model. METHODS: The clinical and imaging data of 332 patients (352 lesions) with PHEO or LPA confirmed by surgical pathology in the Affiliated Hospital of Qingdao University were retrospectively analyzed. The region of interest (ROI) on conventional and enhanced CT images was delineated using ITK-SNAP software. Different radiomics signa…

Research on automatic recognition radiomics algorithm for early sacroiliac arthritis based on sacroiliac MRI imaging.

OBJECTIVE: To create an automated machine learning model using sacroiliac joint MRI imaging for early sacroiliac arthritis detection, aiming to enhance diagnostic accuracy. METHODS: We conducted a retrospective analysis involving 71 patients with early sacroiliac arthritis and 85 patients with normal sacroiliac joint MRI scans. Transverse T1WI and T2WI sequences were collected and subjected to radiomics analysis by two physicians. Patients were randomly divided into training and test groups at a 7:3 ratio. Initially, we extracted the region of interest on the sacroiliac joint surface using IT…

Radiomics based on diffusion tensor imaging and 3D T1-weighted MRI for essential tremor diagnosis.

BACKGROUND: Due to the absence of biomarkers, the misdiagnosis of essential tremor (ET) with other tremor diseases and enhanced physiologic tremor is very common in practice. Combined radiomics based on diffusion tensor imaging (DTI) and three-dimensional T1-weighted imaging (3D-T1) with machine learning (ML) give a most promising way to identify essential tremor (ET) at the individual level and further reveal the potential imaging biomarkers. METHODS: Radiomics features were extracted from 3D-T1 and DTI in 103 ET patients and 103 age-and sex-matched healthy controls (HCs). After data dimensi…

Model Based on Ultrasound Radiomics and Machine Learning to Preoperative Differentiation of Follicular Thyroid Neoplasm.

OBJECTIVES: To evaluate the value of radiomics based on ultrasonography in differentiating follicular thyroid carcinoma (FTC) and follicular thyroid adenoma (FTA) and construct a tool for preoperative noninvasive predicting FTC and FTA. METHODS: The clinical data and ultrasound images of 389 patients diagnosed with FTC or FTA postoperatively were retrospectively analyzed at 3 institutions from January 2017 to December 2023. Patients in our hospital were randomly assigned in a 7:3 ratio to training cohort and validation cohort. External test cohort consisted of data collected from other 2 hosp…

Glioblastoma and Solitary Brain Metastasis: Differentiation by Integrating Demographic-MRI and Deep-Learning Radiomics Signatures.

BACKGROUND: Studies have shown that deep-learning radiomics (DLR) could help differentiate glioblastoma (GBM) from solitary brain metastasis (SBM), but whether integrating demographic-MRI and DLR features can more accurately distinguish GBM from SBM remains uncertain. PURPOSE: To construct and validate a demographic-MRI deep-learning radiomics nomogram (DDLRN) integrating demographic-MRI and DLR signatures to differentiate GBM from SBM preoperatively. STUDY TYPE: Retrospective. POPULATION: Two hundred and thirty-five patients with GBM (N = 115) or SBM (N = 120), randomly divided into a trai…

Radiomics features from 3D-MPRAGE imaging can differentiate temporal-plus epilepsy from temporal lobe epilepsy.

OBJECTIVE: This study aimed to differentiate temporal-plus epilepsy (TPE) from temporal lobe epilepsy (TLE) using extraction of radiomics features from three-dimensional magnetization-prepared rapid acquisition gradient echo (3D-MPRAGE) imaging data. METHODS: Data from patients with TLE or TPE who underwent epilepsy surgery between January 2019 and January 2021 were retrospectively analyzed. Thirty-three regions of interest in the affected hemisphere of each patient were defined on 3D-MPRAGE images. A total of 3531 image features were extracted from each patient. Four feature selection method…

Predicting Malignancy of Thyroid Micronodules: Radiomics Analysis Based on Two Types of Ultrasound Elastography Images.

RATIONALE AND OBJECTIVES: To develop a multimodal ultrasound radiomics nomogram for accurate classification of thyroid micronodules. MATERIALS AND METHODS: A retrospective study including 181 thyroid micronodules within 179 patients was conducted. Radiomics features were extracted from strain elastography (SE), shear wave elastography (SWE) and B-mode ultrasound (BMUS) images. Minimum redundancy maximum relevance and least absolute shrinkage and selection operator algorithms were used to select malignancy-related features. BMUS, SE, and SWE radiomics scores (Rad-scores) were then constructed.…