Noncontrast CT-based deep learning for predicting intracerebral hemorrhage expansion incorporating growth of intraventricular hemorrhage.

Intracerebral hemorrhage (ICH) is a severe form of stroke with high mortality and disability, where early hematoma expansion (HE) critically influences prognosis. Previous studies suggest that revised hematoma expansion (rHE), defined to include intraventricular hemorrhage (IVH) growth, provides improved prognostic accuracy. Therefore, this study aimed to develop a deep learning model based on noncontrast CT (NCCT) to predict high-risk rHE in ICH patients, enabling timely intervention.

A prognostic model integrating radiomics and deep learning based on CT for survival prediction in laryngeal squamous cell carcinoma.

Accurate prognostic prediction is crucial for patients with laryngeal squamous cell carcinoma (LSCC) to guide personalized treatment strategies. This study aimed to develop a comprehensive prognostic model leveraging clinical factors alongside radiomics and deep learning (DL) based on CT imaging to predict recurrence-free survival (RFS) in LSCC patients. We retrospectively enrolled 349 patients with LSCC from Center 1 (training set: n = 189; internal testing set: n = 82) and Center 2 (external testing set: n = 78).

Dual-layer spectral detector CT electron density imaging: impressive technology for imaging characteristics of ground-glass nodules.

Background: Pulmonary ground-glass nodules (GGNs) are increasingly being detected as manifestations of early lung cancer. However, conventional computed tomography (CT) images may fail to clearly show small lesion details, particularly in the case of small, low-density GGNs, which are often difficult to observe. Limited evidence suggests that dual-layer spectral detector CT (SDCT) electron density imaging (EDI) improves the visualization of ground-glass opacities (GGOs) and increases the detection rate of mixed GGNs (mGGNs).

Exploring the relationships between CT and pathological characteristics and gene mutations in neoplastic ground glass nodules.

Background: Neoplastic ground glass nodules (GGNs) are relatively indolent tumors, with slow progression in invasiveness and computed tomography (CT) features. This study aimed to explore the correlation between pathological and CT characteristics and gene mutations in neoplastic GGNs.

Methods: We retrospectively analyzed 1,348 neoplastic GGNs from January 2019 to November 2022, including 290 adenocarcinomas in situ (AIS), 448 microinvasive adenocarcinomas (MIA), and 610 invasive adenocarcinomas (IAC).

Risk factors of breast cancer patients developing multiple primary cancers: a retrospective study and establishing/testing of machine learning models.

Background: Breast cancer is a prevalent malignancy globally, with approximately 1 in 10 breast cancer patients at risk of developing additional primary malignant tumors. This study seeks to explore the risk factors linked to the development of multiple primary cancers (MPCs) in breast cancer patients and to develop predictive models to aid in clinical decision-making.

Methods: A cohort of patients from the Surveillance, Epidemiology, and End Results (SEER) database was analyzed to identify key factors contributing to the occurrence of MPCs.

Mapping the research trends and hotspots on solid pulmonary nodules from 2000-2024: insights from bibliometric analysis.

Background: Due to extensive low-dose computed tomography (CT) screening, the detection rate of solid pulmonary nodules (PNs) is constantly rising. However, existing studies lack a comprehensive understanding of its emerging research trends and interdisciplinary progress, especially in terms of artificial intelligence (AI) integration and molecular characteristics. In this study, we conduct a comprehensive bibliometric analysis to clarify the current research trends and predict future research hotspots in this field, in order to improve diagnostic accuracy, reduce overtreatment, and promote innovation in precise chest oncology.

Exploring the key clinical and CT characteristics of granulomas mimicking peripheral lung cancers: a case-control study.

Objectives: Some granulomas exhibit CT manifestations similar to those of peripheral lung cancers (PLCs), often resulting in misdiagnosis. This study aimed to identify the key clinical and CT indicators for differentiating them.

Materials And Methods: From October 2019 to July 2024, 204 atypical granulomas (no calcification, satellite lesions, and/or halo sign) and 204 size-matched PLCs manifested as solid nodules (SNs) were retrospectively enrolled.

Performance Evaluation of Four Deep Learning-Based CAD Systems and Manual Reading for Pulmonary Nodules Detection, Volume Measurement, and Lung-RADS Classification Under Varying Radiation Doses and Reconstruction Methods.

Optimization of pulmonary nodule detection across varied imaging protocols remains challenging. We evaluated four DL-CAD systems and manual reading with volume rendering (VR) for performance under varying radiation doses and reconstruction methods. VR refers to a post-processing technique that generates 3D images by assigning opacity and color to CT voxels based on Hounsfield units.

A Study on the Selection of Surgical Methods for ≤2cm Lung Adenocarcinomas Based on High-resolution CT Features Combined with Solid Component Size on MPVR.

Rationale And Objectives: The precise treatment methods for small early-stage lung cancer remain unclear. This study aims to analyze the high-resolution computed tomography (HRCT) imaging features and the size of the solid component in multiplanar volume reconstruction (MPVR) in depth, to select the optimal surgical approach (lobar resection or sublobar resection) for patients with lung adenocarcinomas ≤2cm, thereby optimizing clinical treatment strategies and improving patient prognosis.

Methods: A retrospective cohort of 657 consecutive patients with surgically resected lung adenocarcinoma was analyzed at the First Affiliated Hospital of Chongqing Medical University (2014-2023), comprising 345 lobar resection (52.

Exploring and verifying key thin-section computed tomography features for accurately differentiating granulomas and peripheral lung cancers.

Background: Granulomas were frequently misdiagnosed as peripheral lung cancers (PLCs) due to their similarities in imaging findings. This study aimed to establish a classification system based on thin-section computed tomography (TSCT) features for distinguishing granulomas from PLCs.

Methods: From January 2012 to November 2023, 561 granulomas and 561 size-matched PLCs manifested as solid nodules (SNs) were retrospectively enrolled.

A Comprehensive Study of Part-Solid Lung Adenocarcinoma with Lymph Node Metastasis: Clinical, Pathological, and Radiological Perspectives.

Purpose: Compared to solid lung adenocarcinomas (LUADs), part-solid LUADs rarely exhibit lymph node metastasis (LNM) and generally have a favorable prognosis. This study aims to comprehensively investigate the clinical, pathological, and CT characteristics of part-solid LUADs with LNM.

Patients And Methods: This study collected 70 pathologically confirmed part-solid LUADs at two centers, including 35 cases with LNM and 35 matched cases without LNM based on size, CT pattern, and pathological subtype.

Predicting the regrowth of residual uterine fibroids after high-intensity focused ultrasound treatment: an interpretable magnetic resonance imaging radiomics model.

Background: The evaluation of residual uterine fibroids (RFs) after magnetic resonance imaging (MRI)-based radiomics is complex, making it challenging to accurately predict and interpret the regrowth of RFs following high-intensity focused ultrasound (HIFU) treatment. Therefore, the aim of this research was to establish a robust multiparametric radiomics model which functions to predict the regrowth of RFs following HIFU treatment. Moreover, SHapley Additive exPlanations (SHAP) was adopted to clarify the internal prediction process of the model.

Deep learning feature-based model for predicting lymphovascular invasion in urothelial carcinoma of bladder using CT images.

Objectives: Lymphovascular invasion significantly impacts the prognosis of urothelial carcinoma of the bladder. Traditional lymphovascular invasion detection methods are time-consuming and costly. This study aims to develop a deep learning-based model to preoperatively predict lymphovascular invasion status in urothelial carcinoma of bladder using CT images.

Diagnostic Challenges and Insights in Optic Nerve Hemangioblastoma using Magnetic Resonance Imaging: A Case Report.

Background: Optic nerve hemangioblastoma (ONH) is a rare benign tumor. It can be sporadic or associated with Von-Hippel Lindau (VHL) syndrome. Magnetic resonance imaging (MRI) is the most commonly used diagnostic technique for the tumor.

Multiparameter MRI-based model integrating radiomics and deep learning for preoperative staging of laryngeal squamous cell carcinoma.

The accurate preoperative staging of laryngeal squamous cell carcinoma (LSCC) provides valuable guidance for clinical decision-making. The objective of this study was to establish a multiparametric MRI model using radiomics and deep learning (DL) to preoperatively distinguish between Stages I-II and III-IV of LSCC. Data from 401 histologically confirmed LSCC patients were collected from two centers (training set: 213; internal test set: 91; external test set: 97).

Effect of lung inflation states on chest CT image quality and pulmonary nodule detection with visualized respiratory Indicator.

Background: Parts of lung cancer screening guidelines describe the specific scanning protocol of low dose CT (LDCT), among which the requirement for respiratory state is full inspiration end-breath hold. The main focus of lung cancer screening is to evaluate and follow-up pulmonary nodule (PN), so the display and detection of PNs are important. To achieve full inspiration, strict breathing training is required for patients.

Endovaginal coil combined with pelvic array coil magnetic resonance imaging for the preoperative staging of cervical cancer.

Background: Accurate staging of cervical cancer via conventional magnetic resonance imaging (MRI) remains challenging, suggesting a greater need for coil placement closer to the region of interest. This study aimed to determine the value of 3.0-T MRI with an endovaginal coil combined with the pelvic array coil (combined coil) in the preoperative staging of cervical cancer and its correlation with histopathology.

Comparing Random Survival Forests and Cox Regression for Nonresponders to Neoadjuvant Chemotherapy Among Patients With Breast Cancer: Multicenter Retrospective Cohort Study.

Background: Breast cancer is one of the most common malignancies among women worldwide. Patients who do not achieve a pathological complete response (pCR) or a clinical complete response (cCR) post-neoadjuvant chemotherapy (NAC) typically have a worse prognosis compared to those who do achieve these responses.

Objective: This study aimed to develop and validate a random survival forest (RSF) model to predict survival risk in patients with breast cancer who do not achieve a pCR or cCR post-NAC.

3D segmentation of uterine fibroids based on deep supervision and an attention gate.

Introduction: The segmentation of uterine fibroids is very important for the treatment of patients. However, uterine fibroids are small and have low contrast with surrounding tissue, making this task very challenging. To solve these problems, this paper proposes a 3D DA- VNet automatic segmentation method based on deep supervision and attention gate.

Use of deep learning-based high-resolution magnetic resonance to identify intracranial and extracranial symptom-related plaques.

This study aims to develop a deep learning model using high-resolution vessel wall imaging (HR-VWI) to differentiate symptom-related intracranial and extracranial plaques, which is crucial for stroke treatment and prevention. We retrospectively analyzed HR-VWI data from 235 patients, dividing them into a training set (n = 156) and a testing set (n = 79). Using T1-weighted and contrast-enhanced T1WI images, we constructed five deep learning models and selected the best-performing DenseNet 201 model to extract features.

Automatic phantom-less calibration of routine CT scans for the evaluation of osteoporosis and hip fracture risk.

Background/purpose: The diagnosis of osteoporosis remains a paramount concern for orthopedic surgeons worldwide. We aim to (1) evaluate the efficacy of automatic phantom-less quantitative computed tomography (PL-QCT) in diagnosing osteoporosis and (2) investigate its clinical value in predicting hip fracture risk.

Methods: A cohort of 705 patients was included in the study.

Abnormal sagittal patellar tilt during active knee flexion and extension on 4DCT might be associated with patellofemoral pain.

Background: The quadriceps during activities mainly affect patellar movement in the sagittal plane. This study was to analyze the pattern of sagittal patellar tilt in patellofemoral pain (PFP) patients by four-dimensional computed tomography (4DCT).

Methods: Thirty-four knees of PFP patients and 34 control knees were scanned with 4DCT.

Significance of the Lesion-Pleura Relationship in Differentiating Peripheral Inflammatory Lesions and Lung Cancers.

Purpose: To determine the significance of lesion-pleura relationship in differentiating peripheral inflammatory lesions (PILs) and peripheral lung cancers (PLCs).

Patients And Methods: From January 2017 to April 2022, a total of 743 patients with 501 PLCs and 292 PILs (≥1.5 cm) were retrospectively enrolled.

A stacking ensemble system for identifying the presence of histological variants in bladder carcinoma: a multicenter study.

Purpose: To create a system to enable the identification of histological variants of bladder cancer in a simple, efficient, and noninvasive manner.

Material And Methods: In this multicenter diagnostic study, we retrospectively collected basic information and CT images about the patients concerned from three hospitals. An interactive deep learning-based bladder cancer image segmentation framework was constructed using the Swin UNETR algorithm for further features extraction.

Decreased Association Between Patellar Axial Malalignment and Patellar Height and Increased Association Between Patellar Axial Malalignment and Tibial Tubercle-Trochlear Groove During Weightbearing.

Background: Nonweightbearing preoperative assessments avoid quadriceps contraction that tends to affect patellar motion and appear to be inaccurate in quantifying anatomic factors, which can lead to incorrect corrections and postoperative complications.

Questions/purposes: (1) Does the relationship of patellar axial malalignment and other anatomic factors change during weightbearing? (2) What anatomic factor was most strongly correlated with recurrent patellar dislocation during weightbearing?

Methods: This prospective, comparative, observational study recruited participants at our institution between January 2023 and September 2023. During this time, all patients with recurrent patellar dislocations received both weightbearing and nonweightbearing CT scans; control patients who received unilateral CT scans because of injuries or benign tumors received both weightbearing and nonweightbearing CT scans.