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Infarct core segmentation using U-Net in CT perfusion imaging: a feasibility study.
Acta Radiol
January 2025
Department of Medical Imaging, Dalin Tzu-Chi Hospital, Chiayi, Taiwan.
Background: The wide variability in thresholds on computed tomography (CT) perfusion parametric maps has led to controversy in the stroke imaging community about the most accurate measurement of core infarction.
Purpose: To investigate the feasibility of using U-Net to perform infarct core segmentation in CT perfusion imaging.
Material And Methods: CT perfusion parametric maps were the input of U-Net, while the ground truth segmentation was determined based on diffusion-weighted imaging (DWI).
Objectives: The pairing of immunotherapy and radiotherapy in the treatment of locally advanced nonsmall cell lung cancer (NSCLC) has shown promise. By combining radiotherapy with immunotherapy, the synergistic effects of these modalities not only bolster antitumor efficacy but also exacerbate lung injury. Consequently, developing a model capable of accurately predicting radiotherapy- and immunotherapy-related pneumonitis in lung cancer patients is a pressing need.
View Article and Find Full Text PDFWhen multiple instance learning meets foundation models: Advancing histological whole slide image analysis.
Med Image Anal
January 2025
Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China; Medical Research Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Southern Medical University, Guangzhou, China. Electronic address:
Deep multiple instance learning (MIL) pipelines are the mainstream weakly supervised learning methodologies for whole slide image (WSI) classification. However, it remains unclear how these widely used approaches compare to each other, given the recent proliferation of foundation models (FMs) for patch-level embedding and the diversity of slide-level aggregations. This paper implemented and systematically compared six FMs and six recent MIL methods by organizing different feature extractions and aggregations across seven clinically relevant end-to-end prediction tasks using WSIs from 4044 patients with four different cancer types.
View Article and Find Full Text PDFAssessment of intratumor heterogeneity for preoperatively predicting the invasiveness of pulmonary adenocarcinomas manifesting as pure ground-glass nodules.
Quant Imaging Med Surg
January 2025
Department of Clinical Engineering, Nanfang Hospital, Southern Medical University, Guangzhou, China.
Background: The increased use of low-dose computed tomography (CT) for lung cancer screening has improved the detection of ground-glass nodules. However, as the clinical utility of CT findings to predict the invasiveness of pure ground-glass nodules (pGGNs) is currently limited, differentiating pGGNs that indicate invasive adenocarcinoma (IAC) from those that represent other histological entities is challenging. We aimed to quantify intratumor heterogeneity of lung adenocarcinomas characterized by pGGNs on CT to assess its efficacy in predicting IACs before surgery.
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