Breast Cancer

Early Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer using a Longitudinal US-based Stack-model.

RATIONALE AND OBJECTIVES: To evaluate the diagnostic performance of a longitudinal ultrasound (US)-based stack-model for early prediction of pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer, as well as its practicality in assisting radiologists with diagnostic ability. MATERIALS AND METHODS: A total of 974 patients who underwent NAC were retrospectively inclu...

Geometric-structural multi-label learning for non-invasive prediction of breast cancer biomarkers.

Accurate profiling of molecular biomarkers, including ER, PR, HER2, and Ki-67, is pivotal for tailoring therapeutic strategies in breast cancer management. However, conventional determination relies on invasive tissue biopsies, which are costly, time-consuming, and often limited by intratumoral heterogeneity. To address these challenges, this study proposes a non-invasive framework termed Geometri...

ADC Target Profiling in NSCLC: Generalizable AI Separates TROP-2 and cMET Phenotypes.

PURPOSE: Antibody-drug conjugates (ADC) targeting trophoblast cell surface antigen 2 (TROP-2) and cMET are entering clinical trials in non-small cell ...

Explanation-Guided Reconstruction of Missing Clinical Features for Survival Prediction in Pancreatic Cancer.

Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal cancers, with survival rates influenced by a variety of factors, including earl...

Optimisation of radiation dose and image quality in paediatric chest and abdominal CT examinations: A systematic review.

INTRODUCTION: Computed tomography (CT) is indispensable for the rapid evaluation of paediatric chest and abdominal pathology, yet it delivers relative...

The Big Data Science Center at the Shanghai Synchrotron Radiation Facility: the architecture of the superfacility.

In the context of the global big data deluge, concerted efforts are being made to address the challenges faced by large scientific facilities. These e...

Uncertainty quantification of U-Net based segmentation tool using conformal prediction.

BACKGROUND: The radiation therapy treatment process is very labour intensive, and artificial intelligence (AI) based auto contouring tools are increas...

Deep Unsupervised Domain Adaptation for Translating Cancer Dependency Maps From Cell Lines to Breast Cancer Tumor Genomics.

The Cancer dependency maps (DepMap) identify genetic dependencies in cancer cells using large-scale loss-of-function screens, providing a foundation f...

Machine Learning-based Prediction of Unplanned Acute Care in Outpatients Receiving S-1 Chemotherapy.

BACKGROUND/AIM: The increasing use of oral anticancer agents in outpatient settings has led to a growing need for unplanned acute care (UAC) due to tr...

Evidence-Based Strategies for Addressing Cancer- and Treatment-Related Cognitive Impairment: A Review.

Cancer-related cognitive impairment (CRCI) has become a notable long-term consequence for cancer survivors, especially among those receiving chemother...

Radiomics-based causal machine learning for exploratory treatment-effect estimation of neoadjuvant chemotherapy cycle intensity in osteosarcoma: a proof-of-concept study.

BACKGROUND: Radiomics-based modeling has shown promise for characterizing tumor heterogeneity, but its integration with causal machine learning for tr...

Trajectory analysis of sleep disorders and anxiety-depression in female breast cancer patients undergoing chemotherapy: based on group-based Multi-Trajectory Model and machine learning.

BACKGROUND: Sleep disorders and anxiety-depression symptoms can significantly impair the quality of life and treatment adherence among breast cancer p...

Multi-omics Signature Predicts Anti-EGFR Therapy Benefit in Colorectal Cancer Liver Metastases: A Multi-center Cohort Study.

INTRODUCTION: Clinical management of RAS wild-type colorectal cancer liver metastases (CRLM) remains challenging because many patients exhibit primary...

Semi-supervised YOLO-DEP for high-resolution X-ray component localization and counting.

Accurate localization and counting of tiny electronic components in high-resolution X-ray images is a critical yet challenging task in nuclear science...

Preoperative CT-based radiomics of suprapancreatic adipose tissue for predicting high-difficulty lymph node dissection in gastric cancer.

PURPOSE: Preoperative assessment of lymph node dissection (LND) difficulty in gastric cancer remains challenging. Conventional clinical indicators are...

Lung ultrasound in interstitial lung disease: from bedside screening to prognostic integration and AI-assisted standardization.

Interstitial lung diseases (ILDs) require early recognition and longitudinal assessment, yet repeated high-resolution computed tomography (HRCT) is of...

Integrating Machine Learning and Single-Cell Analysis to Reveal the Diagnostic and Therapeutic Value of Regulated Cell Death Mechanisms in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) treatment faces significant challenges, particularly in tumor growth, metastasis, and drug resistance. While several pr...

Comparative study of radiomics features between ASL and DSC perfusion-weighted imaging in distinguishing radiation-induced brain injury from glioma recurrence.

OBJECTIVE: To compare the radiomics features of pseudocontinuous arterial spin labeling (ASL) and dynamic susceptibility contrast (DSC) perfusion-weig...

Artificial Intelligence in Gynecologic Oncology: Current Applications, Clinical Challenges, and Future Perspectives.

Cervical, endometrial, ovarian, vulvar, vaginal, fallopian tube, and gestational trophoblastic neoplasia (GTN) are major gynecologic cancers that sign...

Neural Representation Learning for Compact and Efficient Modeling of Monte Carlo Phase Space Data.

PURPOSE: Monte Carlo (MC) simulations provide gold standard dose calculations in radiation therapy but generate large phase space (PHSP) files that li...