Aim: Dedifferentiated and undifferentiated endometrial carcinoma (DC/UC) is a rare subtype of endometrial cancer characterized by undifferentiated carcinoma components. This study aimed to investigate diagnostic discrepancies and delays in DC/UC and compare them with low-grade endometrioid carcinoma (LGEC).
Methods: We retrospectively analyzed 20 DC/UC and 40 LGEC cases finally diagnosed at our hospital (2016-2024). We compared the data of the two groups, including clinicopathologic characteristics and diagnostic intervals defined as the time from the date of initial biopsy to the date of definitive diagnosis. We assessed diagnostic discordances between preoperative diagnoses, including radiological, clinical, and biopsy, and final diagnoses with immunohistochemical analyses.
Results: DC/UC cases exhibited significantly longer diagnostic intervals (median 46 vs. 5 days, p = 0.037) and required more biopsy attempts (median two vs. 1, p = 0.002) and immunohistochemical tests (median 19 vs. 6, p = 0.001) than LGEC cases. In preoperative diagnoses, 60% of DC/UC cases showed diagnostic discrepancies. Radiological findings frequently suggested uterine sarcoma in DC/UC (30%, 6/20). Only 50% of DC/UC were suggested via initial biopsy. Immunohistochemistry revealed mismatch repair deficiency in 70% of DC/UC cases.
Conclusions: Frequent diagnostic discrepancies and delays were observed in DC/UC, possibly due to its atypical imaging and histopathological features. Raising awareness of DC/UC's clinical and pathological characteristics is crucial to minimizing diagnostic delays. Given its frequency (at least 1% of endometrial cancers) and eligibility for emerging therapies, prioritizing DC/UC in differential diagnoses and improving diagnostic workflows through interdisciplinary collaboration are required for timely and effective treatment.
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http://dx.doi.org/10.1111/jog.16260 | DOI Listing |
Am Surg
March 2025
Department of Surgery, Sapienza University of Rome, Rome, Italy.
BackgroundLarge language models (LLMs) are advanced tools capable of understanding and generating human-like text. This study evaluated the accuracy of several commercial LLMs in addressing clinical questions related to diagnosis and management of acute cholecystitis, as outlined in the Tokyo Guidelines 2018 (TG18). We assessed their congruence with the expert panel discussions presented in the guidelines.
View Article and Find Full Text PDFJ Am Chem Soc
March 2025
CAS Key Laboratory of Bio-Inspired Materials and Interfacial Science, Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Beijing 100190, P. R. China.
Accurate diagnosis of early gastric cancer is valuable for asymptomatic populations, while current endoscopic examination combined with pathological tissue biopsy often encounters bottlenecks for early-stage cancer and causes pain to patients. Liquid biopsy shows promise for noninvasive diagnosis of early gastric cancer; however, it remains a challenge to achieve accurate diagnosis due to the lack of highly sensitive and specific biomarkers. Herein, we propose a protocol combining metabolomics profiling from plasma extracellular vesicles (EVs) and machine learning to identify the metabolomics discrepancies of early gastric cancer individuals from other populations.
View Article and Find Full Text PDFAnal Bioanal Chem
March 2025
Division of Chemical Metrology and Analytical Science, National Institute of Metrology (Key Laboratory of Chemical Metrology and Applications On Nutrition and Health for State Market Regulation), Beijing, 100029, China.
Carcinoembryonic antigen (CEA) is among the earliest identified tumor markers and remains extensively utilized in the diagnosis and management of colorectal cancer. The detection of CEA presents considerable challenges in the field of analytical chemistry, given its complexity. The most prevalent detection approach is the immunoassay, including the chemiluminescence immunoassay commonly employed in clinical settings; however, discrepancies between various methods persist.
View Article and Find Full Text PDFJ Med Internet Res
March 2025
Department of Thoracic Surgery, West China Hospital of Sichuan University, Chengdu, China.
Background: Systematic reviews and meta-analyses rely on labor-intensive literature screening. While machine learning offers potential automation, its accuracy remains suboptimal. This raises the question of whether emerging large language models (LLMs) can provide a more accurate and efficient approach.
View Article and Find Full Text PDFNeurosci Bull
March 2025
State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Division of Psychology, Beijing Normal University, Beijing, 100875, China.
While multiple step saccades (MSS) are occasionally reported in the healthy population, they are more evident in patients with Parkinson's disease (PD). Therefore, MSS has been suggested as a biological marker for the diagnosis of PD. However, the lack of clarity on the neural mechanism underlying the generation of MSS largely impedes their application in the clinic.
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