Gynecologic cancers collectively account for 15.2 % of cancer cases annually and remain the leading cause of cancer-related death among female patients worldwide [1]. They are associated with a substantial health care burden with over 8.9 million disability-adjusted life years globally [2]. A substantial proportion of patients undergoing treatment for gynecologic cancer experience complications, with 83.8 % facing persistent or long-term effects beyond the treatment period, highlighting the need…
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Prediction of histopathology in colon cancer by multidetector computed tomography stratified by tumour side and microsatellite status: A national register study
Colon cancer is one of the most prevalent malignancies, with right-sided and left-sided colon cancers exhibiting distinct clinicopathological and molecular characteristics that influence their behaviour and prognosis. A key difference lies in the prevalence of microsatellite instability-high (MSI), a condition resulting from a deficiency of the mismatch repair (MMR) system or inactivation of the MMR system by hypermethylation of the MLH1 gene. MSI is more common in right-sided colon cancer, with…
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Predicting ADC map quality from T2-weighted MRI: A deep learning approach for early quality assessment to assist point-of-care
Poor quality prostate MRI images compromise diagnostic accuracy, with diffusion-weighted imaging and the resulting apparent diffusion coefficient (ADC) maps being particularly vulnerable. These maps are critical for prostate cancer diagnosis, yet current methods relying on standardizing technical parameters fail to consistently ensure image quality. We propose a novel deep learning approach to predict low-quality ADC maps using T2-weighted (T2W) images, enabling real-time corrective intervention…
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MRI-based radiomics predicts the pathologic response of colorectal liver metastases to systemic therapy: A multicenter study
Colorectal cancer is the third most common cancer worldwide, and the liver is the primary site for metastases [1]. Surgical resection with perioperative chemotherapy is currently the cornerstone in treating colorectal liver metastases (CRLM) [2]. Preoperative systemic therapy has proven to be critical, demonstrating significant efficacy in tumor downsizing, micrometastases sterilization, and patient selection [3–6]. The modification of CRLM during chemotherapy is one of the strongest determinant…
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Impact of the use of deep-learning 3D camera on workflow and patient positioning in CT examinations
CT examinations are one of the main imaging modalities that play an important role in routine medical practice. They are widely performed for the diagnosis of diseases, determination of treatment plans, post-treatment evaluation, and follow-up monitoring, and their clinical significance has been established. In particular, unenhanced chest-abdomen-pelvis CT examinations are commonly used because they are relatively easy to access and can provide a large amount of information. The Organization fo…
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Preoperative focal breast edema on T2-weighted MRI as a predictor of sentinel lymph node metastasis in clinically node-negative T1-2 breast cancer: A retrospective bicentric study
Axillary lymph node metastasis is a critical prognostic factor for the pathological staging, prognosis, and guiding systemic therapy in patients with invasive breast cancer [1]. Over the past decades, the management of axillary lymph nodes in breast cancer patients has evolved significantly. Sentinel lymph node biopsy (SLNB) has replaced axillary lymph node dissection (ALND) as the standard surgical approach, offering similar disease-free survival and overall survival rates with fewer side effec…
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Gadolinium concentration dependent signal enhancement profiles using routine clinical sequences with gadopiclenol, gadoterate, gadobutrol, and gadoxetate at 1.5, 3 and 7 Tesla
Gadolinium-based contrast agents (GBCAs) are widely used in magnetic resonance imaging (MRI) to improve detection and characterization of pathological lesions [1–3]. The presence of current commercial GBCAs in tissues and body fluids most prominently changes images acquired with T1-weighted (T1w) contrast, due to a Gd-induced shortening of corresponding tissue- and fluid-water T1 times [4,5]. The agent-specific parameter that quantitatively captures this effect is the longitudinal molar relaxivi…
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Validation of artificial intelligence software for automatic calcium scoring in cardiac and chest computed tomography
The extent of coronary artery calcium (CAC), assessed on computed tomography (CT), is a strong predictor of cardiovascular events, providing essential data for cardiovascular risk discrimination and decision-making [1–4]. It is now also advised to assess coronary artery calcium on non-electrocardiogram (ECG)-synchronized chest CT, including low-dose chest CT for lung cancer screening (LCS) [5]. While visual evaluation is recommended, quantification of calcium can be considered by calculating the…
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Synthetic hematocrit-based extracellular volume quantification from photon-counting CT: Validation against MRI and systematic bias correction
Myocardial extracellular volume (ECV) is an established imaging biomarker for assessing myocardial fibrosis and interstitial disease, playing a crucial role in the diagnosis and risk stratification of conditions such as cardiomyopathies including amyloidosis, and ischemic heart disease [1,2]. Cardiac magnetic resonance imaging (MRI) has long been considered the reference standard for ECV quantification due to its ability to provide high-resolution tissue characterization through T1 mapping befor…
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A Simplex algorithm approach to determine the optimal keV and window setting for appendicitis visualization
Contrast-enhanced dual energy CT, with low-mono-energetic CT scanning (conventional 70 keV and low-mono energetic 40 keV) can improve visualization of acute inflammatory processes, but the high iodine attenuation often requires adjustment of window display settings to optimize interpretation. Here, we aim to determine optimal keV and window settings for the visualization of acute appendicitis from images derived from low-mono-energetic-CT.