TL;DR
The near-perfect in-site AUC is therefore most plausibly explained by acquisition-related confounding, which extends beyond reconstruction protocol to scanner model and age, and is not evidence of malignancy discrimination.
Histopathology and computed tomography (CT) provide complementary views of renal cancer. We connect these modalities through bidirectional cross-attention between 16 histology prototypes and up to 32 kidney-centered CT tokens from frozen foundation models. In 210 TCGA-KIRC patients with 46 disease-specific survival (DS...
Early and accurate diagnosis of breast cancer has a direct bearing on patient survival, yet interpretation of fine needle aspirate (FNA) imaging features still depends heavily on clinician experience and remains subject to inter-observer disagreement. This paper presents an empirical case study evaluating two supervise...
Background Pure ground-glass nodules (pGGNs) are increasingly detected on chest CT. Although most exhibit indolent behavior, a subset may harbor invasive features before obvious radiological progression, making early risk stratification clinically important. Purpose To develop and externally validate a CT-based deep le...
A model integrating radiomics and clinical data may serve as a potentially effective tool for preoperative risk stratification of mediastinal tumors; this model shows great promise for optimizing individualized surgical planning.
Background/Objective: Lung cancer remains a leading cause of cancer-related mortality, and access to low-dose CT screening is limited in resource-constrained settings. This study aimed to develop and evaluate an interpretable machine learning model for preliminary, symptom-based lung cancer risk discrimination using cl...
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