Purpose: Patients with metastatic adenocarcinoma of unknown origin are a common clinical problem. Knowledge of the primary site is important for their management, but histologically, such tumors appear similar. Better diagnostic markers are needed to enable the assignment of metastases to likely sites of origin on pathologic samples.
Experimental Design: Expression profiling of 27 candidate markers was done using tissue microarrays and immunohistochemistry. In the first (training) round, we studied 352 primary adenocarcinomas, from seven main sites (breast, colon, lung, ovary, pancreas, prostate and stomach) and their differential diagnoses. Data were analyzed in Microsoft Access and the Rosetta system, and used to develop a classification scheme. In the second (validation) round, we studied 100 primary adenocarcinomas and 30 paired metastases.
Results: In the first round, we generated expression profiles for all 27 candidate markers in each of the seven main primary sites. Data analysis led to a simplified diagnostic panel and decision tree containing 10 markers only: CA125, CDX2, cytokeratins 7 and 20, estrogen receptor, gross cystic disease fluid protein 15, lysozyme, mesothelin, prostate-specific antigen, and thyroid transcription factor 1. Applying the panel and tree to the original data provided correct classification in 88%. The 10 markers and diagnostic algorithm were then tested in a second, independent, set of primary and metastatic tumors and again 88% were correctly classified.
Conclusions: This classification scheme should enable better prediction on biopsy material of the primary site in patients with metastatic adenocarcinoma of unknown origin, leading to improved management and therapy.
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http://dx.doi.org/10.1158/1078-0432.CCR-04-2236 | DOI Listing |
Sci Rep
December 2024
Department of Medical Ultrasound, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, No. 16766, Jingshi Road, Jinan, 250014, Shandong, People's Republic of China.
This study aimed to explore a deep learning radiomics (DLR) model based on grayscale ultrasound images to assist radiologists in distinguishing between benign breast lesions (BBL) and malignant breast lesions (MBL). A total of 382 patients with breast lesions were included, comprising 183 benign lesions and 199 malignant lesions that were collected and confirmed through clinical pathology or biopsy. The enrolled patients were randomly allocated into two groups: a training cohort and an independent test cohort, maintaining a ratio of 7:3.
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December 2024
Department of Orthopaedics, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou, China.
Osteosarcoma (OS) is the most prevalent secondary sarcoma associated with retinoblastoma (RB). However, the molecular mechanisms driving the interactions between these two diseases remain incompletely understood. This study aims to explore the transcriptomic commonalities and molecular pathways shared by RB and OS, and to identify biomarkers that predict OS prognosis effectively.
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December 2024
Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju, 61186, Republic of Korea.
Polarization-sensitive optical coherence tomography (PS-OCT) measures the polarization state of backscattered light from tissues and provides valuable insights into the birefringence properties of biological tissues. Contrastive unpaired translation (CUT) was used in this study to generate a synthetic PS-OCT image from a single OCT image. The challenges related to extensive data requirements relying on labeled datasets using only pixel-wise correlations that make it difficult to efficiently regenerate the periodic patterns observed in PS-OCT images were addressed.
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December 2024
Artificial Intelligence in Medical Sciences Research Center, Smart University of Medical Sciences, Tehran, Iran.
Failure to predict stroke promptly may lead to delayed treatment, causing severe consequences like permanent neurological damage or death. Early detection using deep learning (DL) and machine learning (ML) models can enhance patient outcomes and mitigate the long-term effects of strokes. The aim of this study is to compare these models, exploring their efficacy in predicting stroke.
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December 2024
School of Electronic Information and Electrical Engineering, Yangtze University, Jingzhou, 434100, Hubei, China.
Emotions play a crucial role in human thoughts, cognitive processes, and decision-making. EEG has become a widely utilized tool in emotion recognition due to its high temporal resolution, real-time monitoring capabilities, portability, and cost-effectiveness. In this paper, we propose a novel end-to-end emotion recognition method from EEG signals, called MSDCGTNet, which is based on the Multi-Scale Dynamic 1D CNN and the Gated Transformer.
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