Prognosticating axillary lymph node metastasis in breast cancer through integrated photoacoustic imaging, ultrasound, and clinical parameters.

Purpose: To develop and validate a predictive model for axillary lymph node metastasis (ALNM) in breast cancer (BC) by integrating clinicopathological factors, ultrasound features, and photoacoustic imaging-derived SO measurements, aiming to improve diagnostic accuracy and provide comprehensive clinical insights.

Methods: A total of 317 BC patients were included, with the cohort split into a training set (70%) and a testing set (30%). Univariate and multivariate logistic regression identified key predictive factors, leading to the creation of three models: ModA (clinicopathological factors only), ModB (clinicopathological and ultrasound features), and ModC (clinicopathological, ultrasound, and SO measurements from photoacoustic imaging).

Enhancing thyroid nodule malignancy prediction: integrating photoacoustic imaging-derived SO2 with clinical and ultrasound data.

Background: Photoacoustic imaging (PAI) has shown promise in diagnosing thyroid nodules. However, current methods rely on subjective visual assessments, lacking quantitative precision.

Purpose: This study evaluates the diagnostic accuracy of PAI in distinguishing benign from malignant thyroid nodules.

Photoacoustic Imaging with Attention-Guided Deep Learning for Predicting Axillary Lymph Node Status in Breast Cancer.

Rationale And Objectives: Preoperative assessment of axillary lymph node (ALN) status is essential for breast cancer management. This study explores the use of photoacoustic (PA) imaging combined with attention-guided deep learning (DL) for precise prediction of ALN status.

Materials And Methods: This retrospective study included patients with histologically confirmed early-stage breast cancer from 2022 to 2024, randomly divided (8:2) into training and test cohorts.

Photoacoustic-Based Intra- and Peritumoral Radiomics Nomogram for the Preoperative Prediction of Expression of Ki-67 in Breast Malignancy.

Rationale And Objectives: This study investigated the preoperative predictive efficiency of radiomics derived from photoacoustic (PA) imaging, integrated with the clinical features of Ki-67 expression in malignant breast cancer (BC), with a focus on both intratumoral and peritumoral regions.

Methods: This study involved 359 patients, divided into a training set (n = 251) and a testing set (n = 108). Radiomic features were extracted from intratumoral and peritumoral regions using PA imaging.

Enhancing Axillary Lymph Node Diagnosis in Breast Cancer with a Novel Photoacoustic Imaging-Based Radiomics Nomogram: A Comparative Study of Peritumoral Regions.

Rationale And Objectives: This study aims to assess the predictive ability of photoacoustic (PA) imaging-based radiomics combined with clinical characteristics for axillary lymph node (ALN) status in early-stage breast cancer patients and to compare performance in different peritumoral regions.

Methods: This study involved 369 patients from Shenzhen People's Hospital, divided into a training set of 295 and a testing set of 74. PA imaging data were collected from all participants, and radiomics analysis was performed on intratumoral and various peritumoral regions.

Ultrasound Radiomics for the Prediction of Breast Cancers with HER2-Zero, -Low, and -Positive Status: A Dual-Center Study.

Purpose: To assess whether gray-scale ultrasound (US) based radiomic features can help distinguish HER2 expressions (ie, HER2-overexpressing, HER2-low-expressing, and HER2-zero-expressing) in breast cancer.

Materials And Methods: This retrospective study encompassed female breast cancer patients who underwent US examinations at two distinct centers from February 2021 to July 2023. Tumor segmentation and radiomic feature extraction were performed on grayscale US images.

Deep learning combined with attention mechanisms to assist radiologists in enhancing breast cancer diagnosis: a study on photoacoustic imaging.

Accurate prediction of breast cancer (BC) is essential for effective treatment planning and improving patient outcomes. This study proposes a novel deep learning (DL) approach using photoacoustic (PA) imaging to enhance BC prediction accuracy. We enrolled 334 patients with breast lesions from Shenzhen People's Hospital between January 2022 and January 2024.

Integrated nomogram to predict HER2 expression in breast tumor: Clinical, Ultrasound, and Photoacoustic imaging approaches.

Background: HER2 is a key biomarker for breast cancer treatment and prognosis. Traditional assessment methods like immunohistochemistry (IHC) and fluorescence in situ hybridization (FISH) are effective but costly and time-consuming. Our model incorporates these methods alongside photoacoustic imaging to enhance diagnostic accuracy and provide more comprehensive clinical insights.

Multimodal PA/US imaging in Rheumatoid Arthritis: Enhanced correlation with clinical scores.

Background: Accurate assessment of Rheumatoid Arthritis (RA) activity remains a challenge. Multimodal photoacoustic/ultrasound (PA/US) joint imaging emerges as a novel imaging modality capable of depicting microvascularization and oxygenation levels in inflamed joints associated with RA. However, the scarcity of large-scale studies limits the exploration of correlating joint oxygenation status with disease activity.