Metastasis to the tongue, duodenum or pancreas from primary lung cancer is uncommon. Primary lung cancer presenting with symptoms related to metastases at these sites, at initial presentation is extremely rare. We report a 45-year-old man with disseminated lung malignancy who presented with dyspepsia, melena, symptoms due to anaemia and swelling in the tongue. Oral examination revealed a hard submucosal anterior tongue lesion. Biopsies from the tongue lesion and the duodenal ulcer seen on upper gastrointestinal endoscopy were suggestive of metastasis from lung primary. CT revealed lung primary with disseminated metastasis to lung, liver, adrenals, kidneys, head and body of pancreas, duodenum and intra-abdominal lymph nodes. The patient was treated with palliative chemotherapy. The unusual presentation and diagnostic details are discussed.
Medical diagnostic reports archived as electronic forms are valuable resources for processing to understand retrospectively, the severity of the disease among patients and to verify the correctness of the diagnosis. In this work, Breast Cancer Pathology reports are processed using Natural Language Processing (NLP) and Information Extraction (IE) techniques in order to extract the parameters required for cancer staging namely Tumour (T), Lymph nodes (N) and Metastases (M). An automated system is developed to process the 'Impression' section of the report, classify T and N using pTNM classification protocol of American Joint Committee on Cancer (AJCC) and derive the stage S of cancer of patients. T and N are classified using numerical parameters and non-numeric medical conditions given in the natural language text. Metastases M which is not evident from Pathology reports is given a default value of M0 for staging. The dataset consisting of 150 de-identified reports were reviewed by the Pathologists to obtain the Gold standard for evaluation. The TNM classification and the cancer stage derived by the system were evaluated against the Gold standard and discrepancy reports were generated. The extraction process was then fine-tuned based on the recommendations of the domain experts.The automatic staging process had an average percentage of 73 for Precision, 82 for Recall, 59 for Specificity and 72 for Accuracy. The lack of high performance is due to presence of certain vital information in other sections of the report that are not processed. Processing these sections in future would improve the performance.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.