Breast Cancer

Optimizing breast cancer therapy: chemoressitance and machine learning for precision prediction.

BACKGROUND: Validate a novel in vitro resistance platform for breast cancer (BC) by assessing resist...

Immunoscore: redefining the landscape of colorectal cancer control and care.

The Immunoscore (IS), introduced by Jerome Galon in the early 2000s, evaluates immune cell densities...

Developing a machine-learning model to enable treatment selection for neoadjuvant chemotherapy for esophageal cancer.

Although neoadjuvant chemotherapy with docetaxel + cisplatin + 5-fluorouracil (CF) has been the stan...

AI-powered prediction model for neoadjuvant chemotherapy efficacy: comprehensive analysis of breast cancer histological images.

Breast cancer patients exhibit variable responses to neoadjuvant therapy (NAT), necessitating robust...

Classification of Renal Lesions by Leveraging Hybrid Features from CT Images Using Machine Learning Techniques.

Renal cancer is amid the several reasons of increasing mortality rates globally, which can be reduce...

Tuning antibody stability and function by rational designs of framework mutations.

Artificial intelligence and machine learning models have been developed to engineer antibodies for s...

Attention-based multimodal deep learning for interpretable and generalizable prediction of pathological complete response in breast cancer.

BACKGROUND: Accurate prediction of pathological complete response (pCR) to neoadjuvant chemotherapy ...

Machine learning analysis of survival outcomes in breast cancer patients treated with chemotherapy, hormone therapy, surgery, and radiotherapy.

Breast cancer continues to be a leading cause of death among women in the world. The prediction of s...

Predicting the prognosis of radical gastrectomy for patients with locally advanced gastric cancer after neoadjuvant chemotherapy using machine learning technology: a multicenter study in China.

BACKGROUND: Neoadjuvant chemotherapy (NAC) can improve the prognosis of patients with locally advanc...

Exploring graph-based models for predicting active compounds against triple-negative breast cancer.

Breast cancer is among the most dominant and rapidly rising cancers, both in India and around the wo...

Prediction of Early Neoadjuvant Chemotherapy Response of Breast Cancer through Deep Learning-based Pharmacokinetic Quantification of DCE MRI.

Purpose To improve the generalizability of pathologic complete response (pCR) prediction following ...

AI Revolution in Radiology, Radiation Oncology and Nuclear Medicine: Transforming and Innovating the Radiological Sciences.

The integration of artificial intelligence (AI) into clinical practice, particularly within radiolog...

Machine learning-driven programmed cell death signature for prognosis and drug candidate discovery in diffuse large B-cell lymphoma: Multi-cohort study and experimental validation.

BACKGROUND: Relapse and drug resistance are major contributor to chemotherapy failure in diffuse lar...

Artificial intelligence-driven discovery of YH395A: A novel TGFβR1 inhibitor with potent anti-tumor activity against triple-negative breast cancer.

Characterized by high malignancy and limited treatment efficacy, triple-negative breast cancer (TNBC...

Robust Bi-CBMSegNet framework for advancing breast mass segmentation in mammography with a dual module encoder-decoder approach.

Breast cancer is a prevalent disease affecting millions of women worldwide, and early screening can ...

MTMedFormer: multi-task vision transformer for medical imaging with federated learning.

Deep learning has revolutionized medical imaging, improving tasks like image segmentation, detection...

Evaluating cell-free DNA integrity index as a non-invasive biomarker for neoadjuvant chemotherapy in colorectal cancer patients.

BACKGROUND: Neoadjuvant chemotherapy (NAC) is gaining attention as a treatment for advanced colorect...

Corrigendum to 'CT-Based Deep Learning Predicts Prognosis in Esophageal Squamous Cell Cancer Patients Receiving Immunotherapy Combined with Chemotherapy' [Acad Radiol 32/6 (2025) 3397-3409].

Dynamic abdominal MRI image generation using cGANs: A generalized model for various breathing patterns with extensive evaluation.

Organ motion is a limiting factor during the treatment of abdominal tumors. During abdominal interve...

Machine learning-assisted high-throughput prediction and experimental validation of high-responsivity extreme ultraviolet detectors.

Identifying materials with optimal optoelectronic properties for targeted applications represents bo...

pyDOSEIA: A Python Package for Radiological Impact Assessment during Long-term or Accidental Atmospheric Releases.

pyDOSEIA is a Python package designed for meteorological data processing and radiological impact ass...