Oncology ImagingArchives
Interpretable machine learning using CT radiomics predicts pathological upgrading after secondary resection in non-muscle-invasive bladder cancer
Oncology Imaging 5 min.
Bladder cancer is a common malignant tumor of the urinary system worldwide, with more than 600,000 new cases and over 200,000 deaths annually. Its incidence and mortality rates rank 10th and 13th among all malignant tumors, respectively [1, 2]. Pathologically, bladder cancer is primarily classified into...
Diagnostic accuracy of FDG-PET-CT to predict axillary lymph node response after neo-adjuvant chemotherapy in lymph node-positive breast cancer patients
Oncology Imaging 5 min.
Worldwide, breast cancer is the most commonly diagnosed malignancy in women [1]. The mainstay of treatment is surgery with adjuvant therapy including chemo, hormonal and radiotherapy. The aims of neo-adjuvant chemotherapy (NAC) include de-escalating surgery and potentially achieving pathological complete...
StructSAM: structure-aware prompt adaptation for robust lung cancer lesion segmentation in CT
Oncology Imaging 8 min.
Lung cancer remains the leading cause of cancer-related mortality worldwide, accounting for approximately one in five cancer deaths1. Accurate delineation of lung lesions from computed tomography (CT) scans plays a pivotal role in the clinical workflow, underpinning early detection, precise staging,...
MRI-based evaluation for predicting pathological response and monitoring reactive angioma evolution in triple-negative breast cancer undergoing neoadjuvant chemoimmunotherapy
Oncology Imaging 5 min.
TNBC is a type of breast cancer in which ER, PR, and human epidermal growth factor receptor2 (Her2) are all negative. The disease progresses rapidly and has a poor prognosis. Neoadjuvant therapy is an important treatment for TNBC, while immunotherapy, particularly anti-PD-1/PD-L1 pathway immune modulators,...
